Learn to program with Python 3, visualize algorithms and data structures, and implement them in Python projects

This course is one of the most comprehensive and beginner-friendly courses on learning to code with Python—one of the top programming languages in the World—and using it to build algorithms and data structures with projects from scratch.

We will walk you step-by-step through the fascinating world of Python programming using visualizations of programs as they execute, algorithms as they run, and data structures as they are constructed. Nothing is left to the imagination; you'll see it all and then build it all.

Since it caters to a broad spectrum of students, the course is split into two parts: part 1 focusing on the Python programming language and part 2 focusing on Algorithms, data structures, performance analysis, and larger-scale projects.

Part 1: Python and programming fundamentals

• Text - Strings

• Numbers - ints and floats

• Execution flow control - branching with if/elif/else

• Compound data types - lists, dictionaries, tuples, and sets

• Iterables and iteration with generators, for and while loops, and more!

• Functions, execution context and frames, and building custom functions

In this course, you'll learn the Python 3 and the PHP programming languages from absolute beginner to advanced in no time at all!

This course is aimed at absolute beginners; you don't need any coding experience at all!

We'll start by downloading and installing Python and the Sublime Text Editor—both for free. This will give you all the tools you need to start writing and running Python code.

For PHP, we'll use a free online development environment so that we can learn web development concepts. Then, we'll delve into very basic computer science concepts, such as:

• String manipulation

• Printing to the screen

• Simple math

• Variables

• Data types (lists, tuples, dictionaries, and more)

• Assignment operators

• Comparison operators

After that, we'll move on to more intermediate topics such as:

• While Loops

• For Loops

• Conditional If/Else Statements

• Fizzbuzz

Finally, we'll finish up with more advanced topics such as:

• Modules

• Functions

• Classes

You'll learn all these features for both Python and PHP. You'll start with Python, and then move over to PHP in the second half of the course. Python and PHP are two of the greatest programming languages, and learning them has never been this easy!

Python programming and Ruby coding are popular for a reason! Become a software coder, web developer, or hacker today

In this course, you'll learn the Python 3 programming language and the Ruby programming language from an absolute beginner level to advanced—in no time at all! This course targets absolute beginners; you don't need any coding experience at all!

We start by setting up, downloading, and installing Python and the Sublime Text Editor—both for free. This will give you all the tools you need to start writing and running Python code.

For Ruby, we'll use a free online development environment (or you can download Ruby yourself onto your computer).

Then, we'll delve into very basic computer science concepts, such as:

• Printing to the screen

• String manipulation

• Variables

• Data types (lists, tuples, dictionaries and more)

• Simple math

• Comparison operators

• Assignment operators

After that, we'll move on to more intermediate topics such as:

• Conditional if/else statements

• While loops

• For loops

• Fizzbuzz

Finally, we'll finish up with more advanced topics like:

• Functions

• Modules

• Classes

You'll learn all these things for both Python and Ruby. We start with Python, and then on to Ruby in the second half of the course.

Welcome to Python GUI Programming – Building Desktop Applications with Tkinter and SQLite

This course will help you build a Python GUI application step-by-step with Tkinter, SQLite. and the free CoinMarketCap API. Throughout the course, you will learn more about Python and Tkinter, including:

• Understanding API nd its usage.

• Extracting cryptocurrency coin data and working with it.

• Building portfolio logic on the command line then focusing on the GUI

This course integrates all the aspects you need to get you on the road to becoming a successful Tkinter developer with proper live practical exercises and walk-throughs that take you from the basics to advanced use.

After completing this course, you will be ready to expand your knowledge of Tkinter and Python.

All the codes and supporting files for this course are available at: https://github.com/PacktPublishing/Python-GUI-Programming---Building-a-Desktop-Application-with-Tkinter-and-SQLite.

A straight-to-the-point Python course to quickly get you started with writing Python code and creating Python programs.

This Python course will get you up and running with Python very quickly saving you time spent googling for video tutorials, exercises and examples. The course is ideal for those who haven't programmed before, but it also works for experienced programmers who want to learn Python as long as they don't get offended by a bit of extra explanations of programming concepts. You will start from scratch by learning all Python fundamentals and slowly progressing into more advanced Python third-party libraries and by the end of the course you will know how to write Python programs. You will actually build your a real Python program that has a graphical user interface (GUI) built with Python. As a bonus you will also learn how to convert that program into an executable that runs both on Windows and Mac as a standalone program so that you can give your program to anyone. The course also guides you on how to go about building even more advanced programs with Python. You will learn how to use Python third-party libraries for building programs in a wide range of application areas. This is not simply a tutorial. The content of the course consists of a mix of video lectures, quizzes, exercises, and discussions with the instructor and the fellow students giving you a complete package to help you become a professional Python programmer. All the code and supporting files for this course are available at -https://github.com/PacktPublishing/Python-for-Beginners-with-Examples

Implement effective programming techniques in Python to build scalable software that saves time and memory

This updated edition features cutting-edge techniques for building effective concurrent applications in Python 3.7. The book introduces parallel programming architectures and covers the fundamental recipes for thread-based and process-based parallelism. You'll learn about mutex, semaphores, locks, queues exploiting the threading, and multiprocessing modules, all of which are basic tools to build parallel applications. Recipes on MPI programming will help you to synchronize processes using the fundamental message passing techniques with mpi4py. Furthermore, you'll get to grips with asynchronous programming and how to use the power of the GPU with PyCUDA and PyOpenCL frameworks. Finally, you'll explore how to design distributed computing systems with Celery and architect Python apps on the cloud using PythonAnywhere, Docker, and serverless applications.

With this book, you will be confident in building concurrent and high-performing applications in Python.

This course integrates all the aspects you need to get you on the road to becoming a successful Tkinter developer with proper live practical exercises and walk-throughs that take you from the basics to advanced use.

With this course, you will be ready to expand your knowledge of Tkinter and Python.

Learn to create a user authentication system in Django to register users, login, log out, edit profiles, and more!

Django and Python can seem overwhelming at first, but they don't have to be! In this course, I'll walk you through them step-by-step and you'll be building your first web app in MINUTES. You'll be amazed how quick and easy it is to create very professional looking websites, even if you have no programming or web design experience at all.

By the time we're finished, you'll have a solid understanding of Django and how to use it to build awesome web apps.

Do you fear regular expressions (regexes)? Do you need to ace the next interview where you will be asked about the advanced usage of regexes? Then you need this course! Regexes are a very powerful tool when you need to deal with textual data. Python offers all the advanced features of regexes such as look-arounds, quantifiers, and commenting your regexes to help your colleagues.

With this course, you will have a better understanding of all Python functions and will be able to apply regex concepts to real-life programming situations.

Use Bayesian analysis and Python to solve data analysis and predictive analytics problems

Bayesian methods have grown recently because of their success in solving hard data analytics problems. They are rapidly becoming a must-have in every data scientists toolkit. The course uses a hands-on method to teach you how to use Bayesian methods to solve data analytics problems in the real world. You will understand the principles of estimation, inference, and hypothesis testing using the Bayesian framework. You will also learn to use them to solve problems such as A/B testing, understanding consumer habits, risk evaluation, adjusting machine learning predictions, reliability analysis, detecting the influence of one variable over an outcome, and many others.

With this course, you will be able to apply and use Bayesian methods as part of your data analytics toolbox, thus helping you use Python to solve a majority of common statistical problems in data science.

These days, everything uses a database, and SQLite is one of the most popular databases out there. Free and open-source, SQLite is a great database for smaller projects, hobby projects, or testing and development.

With this course, you'll build a very basic app to help reinforce all the things you learned along the way.

Learn to create a user authentication system in Django to register users, login, log out, edit profiles, and more!

Django and Python can seem overwhelming at first, but they don't have to be! In this course, I'll walk you through them step-by-step and you'll be building your first web app in MINUTES. You'll be amazed how quick and easy it is to create very professional looking websites, even if you have no programming or web design experience at all.

With this course you'll have a solid understanding of Django and how to use it to build awesome web apps.

Do you fear regular expressions (regexes)? Do you need to ace the next interview where you will be asked about the advanced usage of regexes? Then you need this course! Regexes are a very powerful tool when you need to deal with textual data. Python offers all the advanced features of regexes such as look-arounds, quantifiers, and commenting your regexes to help your colleagues.

With this course, you will have a better understanding of all Python functions and will be able to apply regex concepts to real-life programming situations.

Use Bayesian analysis and Python to solve data analysis and predictive analytics problems

Bayesian methods have grown recently because of their success in solving hard data analytics problems. They are rapidly becoming a must-have in every data scientists toolkit. The course uses a hands-on method to teach you how to use Bayesian methods to solve data analytics problems in the real world. You will understand the principles of estimation, inference, and hypothesis testing using the Bayesian framework. You will also learn to use them to solve problems such as A/B testing, understanding consumer habits, risk evaluation, adjusting machine learning predictions, reliability analysis, detecting the influence of one variable over an outcome, and many others.

With this course, you will be able to apply and use Bayesian methods as part of your data analytics toolbox, thus helping you use Python to solve a majority of common statistical problems in data science.

Recommendation Engines have become an integral part of any application. For accurate recommendations, you require user information. The more data you feed to your engine, the more output it can generate – for example, a movie recommendation based on its rating, a YouTube video recommendation to a viewer, or recommending a product to a shopper online.

In this course, you'll be able to build effective online recommendation engines with Machine Learning and Python – on your own.

Bring your Python skills into the real world. Solve production issues around databases, parallelism, and deployment

Python is simple, but it isn't easy. Python emphasizes code readability, using indentation and whitespaces to create code blocks. This makes it simpler than C++ or Java, where curly braces and keywords are scattered across the code. Python is high-level, which allows programmers like you to create logic with fewer lines of code.

This course follows a problem-solution format to tackle common roadblocks in Python programming. How can we handle large datasets and files, processing them in Python efficiently? How can we address performance issues for long-running tasks?

Data analysts, Machine Learning professionals, and data scientists often use tools such as Pandas, Scikit-Learn, and NumPy for data analysis on their personal computer. However, when they want to apply their analyses to larger datasets, these tools fail to scale beyond a single machine, and so the analyst is forced to rewrite their computation.

In this course, you’ll learn to scale your data analysis. Firstly, you will execute distributed data science projects right from data ingestion to data manipulation and visualization using Dask. Then, you will explore the Dask framework. After, see how Dask can be used with other common Python tools such as NumPy, Pandas, matplotlib, Scikit-learn, and more.

Manipulate and analyze network data with the power of Python and NetworkX

NetworkX is a leading free and open source package used for network science with the Python programming language. NetworkX can track properties of individuals and relationships, find communities, analyze resilience, detect key network locations, and perform a wide range of important tasks. With the recent release of version 2, NetworkX has been updated to be more powerful and easy to use.

With this book, you’ll be able to choose appropriate network representations, use NetworkX to build and characterize networks, and uncover insights while working with real-world systems.

Data Science Projects with Python is designed to give you practical guidance on industry-standard data analysis and machine learning tools in Python, with the help of realistic data. The book will help you understand how you can use pandas and Matplotlib to critically examine a dataset with summary statistics and graphs, and extract the insights you seek to derive.

With this book, you will have the skills you need to confidently use various machine learning algorithms to perform detailed data analysis and extract meaningful insights from unstructured data.

Explore the exciting world of machine learning with the fastest growing technology in the world

Machine learning—the ability of a machine to give right answers based on input data—has revolutionized the way we do business. Applied Supervised Learning with Python provides a rich understanding of how you can apply machine learning techniques in your data science projects using Python. You'll explore Jupyter Notebooks, the technology used commonly in academic and commercial circles with in-line code running support.

With this book, you'll be equipped to not only work with machine learning algorithms, but also be able to create some of your own!

Discover the skill-sets required to implement various approaches to Machine Learning with Python

Unsupervised learning is about making use of raw, untagged data and applying learning algorithms to it to help a machine predict its outcome. With this book, you will explore the concept of unsupervised learning to cluster large sets of data and analyze them repeatedly until the desired outcome is found using Python.

With this book, you will have learned the art of unsupervised learning for different real-world challenges.

Take your financial skills to the next level by mastering cutting-edge mathematical and statistical financial applications

The second edition of Mastering Python for Finance will guide you through carrying out complex financial calculations practiced in the industry of finance by using next-generation methodologies. You will master the Python ecosystem by leveraging publicly available tools to successfully perform research studies and modeling, and learn to manage risks with the help of advanced examples.

With this book, you will also be able to apply Python to different paradigms in the financial industry and perform efficient data analysis.

This fast-paced, action-packed course will maximize your time; it's designed from the ground up to familiarize you with the basics of Python, so you can pursue your data science dreams. With this course, you will be up-and-running with Python Data Science in no time, helping you prove your value and expertise today and build your CV and skill set for tomorrow.

This course will get you up-to-speed with using Python, without resorting to a collection of disconnected, unrelated pieces of information. Thus, you can take the next step toward advancing your career in data science.

For data to be useful and meaningful, it must be curated and refined. Data Wrangling with Python teaches you the core ideas behind these processes and equips you with knowledge of the most popular tools and techniques in the domain.

The course starts with the absolute basics of Python, focusing mainly on data structures. It then delves into the fundamental tools of data wrangling like NumPy and Pandas libraries. You'll explore useful insights into why you should stay away from traditional ways of data cleaning, as done in other languages, and take advantage of the specialized pre-built routines in Python. This combination of Python tips and tricks will also demonstrate how to use the same Python backend and extract/transform data from an array of sources including the Internet, large database vaults, and Excel financial tables. To help you prepare for more challenging scenarios, you'll cover how to handle missing or wrong data, and reformat it based on the requirements from the downstream analytics tool. The course will further help you grasp concepts through real-world examples and datasets.

By the end of this course, you will be confident in using a diverse array of sources to extract, clean, transform, and format your data efficiently.

This course is designed to teach you the basics of Python and Data Science in a practical way, so that you can acquire, test, and master your Python skills gradually.

This course is designed in a practical way to teach you the basics of Python and Data Science. A complete course packed with step-by-step instructions, working examples, and helpful advice. This course is clearly divided into small parts that will help you understand each part individually and help you learn at your own pace.

This course will help you create a RESTful web service right from scratch

RESTful Web Services is a stateless client-server architecture where web services are resources and can be identified by their URIs. Implementing RESTful Web Services with Java helps to simplify the development and deployment of web services.

This course will show you how to implement a REST Web Service (API) in Java from scratch (using servlets), covering the basic concepts. By the end of this course, you will be able to efficiently create reliable and powerful Java RESTful Web Services and get a clear understanding of the process by implementing it your web apps.

Use Python and NLTK (Natural Language Toolkit) to build out your own text classifiers and solve common NLP problems.

If NLP hasn't been your forte, Natural Language Processing Fundamentals will make sure you set off to a steady start. This comprehensive guide will show you how to effectively use Python libraries and NLP concepts to solve various problems.

You'll be introduced to natural language processing and its applications through examples and exercises. This will be followed by an introduction to the initial stages of solving a problem, which includes problem definition, getting text data, and preparing it for modeling. With exposure to concepts like advanced natural language processing algorithms and visualization techniques, you'll learn how to create applications that can extract information from unstructured data and present it as impactful visuals.The book will easily equip you with the knowledge you need to build applications that interpret human language.

Implement concurrency in your apps using Celery, and Pyro, and make your Python apps distributed with AWS

Facing difficulty in implementing concurrent and multithreaded programs in your Python applications? Is this preventing you from implementing efficient code in your apps and benefiting from multiprocessing?

This course will help you resolve these difficulties. You will start by exploring the basic concepts of concurrency and distributed computing, and you'll learn which Python libraries are relevant to these. You will not only learn to see Celery as a way to build-in concurrency into your apps, but also Pyro as an alternative to Celery. You will create processes and manage processes along with interprocess communication; combine coroutines with threads and processes; practice the management of process pools; implement asynchronous tasks/job queues using AsyncResult and Celery backends; invoke remote methods in your Python-based code, and use these skills and concepts when working with AWS for Python.

PyTorch is extremely powerful and yet easy to learn. It provides advanced features, such as supporting multiprocessor, distributed, and parallel computation. This book is an excellent entry point for those wanting to explore deep learning with PyTorch to harness its power.

This book will introduce you to the PyTorch deep learning library and teach you how to train deep learning models without any hassle. We will set up the deep learning environment using PyTorch, and then train and deploy different types of deep learning models, such as CNN, RNN, and autoencoders. You will learn how to optimize models by tuning hyperparameters and how to use PyTorch in multiprocessor and distributed environments. We will discuss long short-term memory network (LSTMs) and build a language model to predict text.

By the end of this book, you will be familiar with PyTorch's capabilities and be able to utilize the library to train your neural networks with relative ease.

We avoid complex math equations, which can often be a barrier to entry for newcomers. This course will teach you to apply deep learning concepts using Python to solve challenging tasks. You'll build a Python deep learning-based image recognition system and deploy and integrate images into web apps or phone apps.

You will start out with an intuitive understanding of neural networks in general. We will guide you through the building blocks of deep learning networks to tackle complex neural networks. So, take this course and learn the skills and temperament need to enter the AI marketplace today.

When it comes to offensive security, your ability to create powerful tools on the fly is indispensable. Python is fast becoming the programming language of choice for hackers, reverse engineers, and software testers because it's easy to write quickly, and it has the low-level support and libraries that make hackers happy. In this course, you’ll explore the darker side of Python’s capabilities—writing network sniffers, manipulating packets, infecting virtual machines, creating stealthy trojans, and more. This course starts from scratch and provides the latest tools and techniques available for Pentesting using Python scripts. We’ll show you the concepts and how to implement hacking tools and techniques such as debuggers, fuzzers, and emulators. You’ll detect sandboxing and automate com-mon malware tasks, such as keylogging and screenshotting.

You’ll be able to escalate Windows privileges with creative process control, use offensive memory forensics tricks to retrieve password hashes, and inject shellcode into a virtual machine. Later, you’ll learn to extend the popular Burp Suite web-hacking tool, abuse Windows COM automation to perform a man-in-the browser attack, and exfiltrate data from a network most sneakily. After completing this course, you’ll understand how to use Python scripts for penetration testing.

With the flexibility and features of scikit-learn and Python, build machine learning algorithms that optimize the programming process and take application performance to a whole new level

As machine learning algorithms become popular, new tools that optimize these algorithms are also developed. Machine Learning Fundamentals explains you how to use the syntax of scikit-learn. You'll study the difference between supervised and unsupervised models, as well as the importance of choosing the appropriate algorithm for each dataset. You'll apply unsupervised clustering algorithms over real-world datasets, to discover patterns and profiles, and explore the process to solve an unsupervised machine learning problem.

The focus of the book then shifts to supervised learning algorithms. You'll learn to implement different supervised algorithms and develop neural network structures using the scikit-learn package. You'll also learn how to perform coherent result analysis to improve the performance of the algorithm by tuning hyperparameters.

Kick-start your development journey with this end-to-end guide that covers Python programming fundamentals along with application development

Python is a cross-platform language used by organizations such as Google and NASA. It lets you work quickly and efficiently, allowing you to concentrate on your work rather than the language. Based on his personal experiences when learning to program, Learn Programming in Python with Cody Jackson provides a hands-on introduction to computer programming utilizing one of the most readable programming languages–Python. It aims to educate readers regarding software development as well as help experienced developers become familiar with the Python language, utilizing real-world lessons to help readers understand programming concepts quickly and easily.

The book starts with the basics of programming, and describes Python syntax while developing the skills to make complete programs. In the first part of the book, readers will be going through all the concepts with short and easy-to-understand code samples that will prepare them for the comprehensive application built in parts 2 and 3. The second part of the book will explore topics such as application requirements, building the application, testing, and documentation. It is here that you will get a solid understanding of building an end-to-end application in Python. The next part will show you how to complete your applications by converting text-based simulation into an interactive, graphical user interface, using a desktop GUI framework. After reading the book, you will be confident in developing a complete application in Python, from program design to documentation to deployment.

Dive into the world of Flask Framework in Python to build an array of simple yet powerful web applications

This course will take you through Flask Microframework; it covers all its components and elements and how to integrate it with useful third-party libraries. You will download all you need to get started with Flask development and then delve straight into the frontend and backend development stack. The course will then show you the general flow in developing a Flask application, including some extensions used by developing a simple application. You will then develop multiple applications such as note-taking, social medias, and file sharing applications.

By the end of the course, you will be able to build your own web applications using Flask in Python.

This course will teach you Python in a practical manner, complete with examples, quizzes, exercises, and more. You will learn when to use Python 2 and Python 3; both versions are covered in the course and you will learn to program in both. You'll learn how to take tedious and repetitious tasks and turn them into programs that will save you time and simplify your life on Linux, UNIX, or MAC systems. You will learn how to prepare your computer for programming in Python; how to work with various data types including strings, lists, tuples, dictionaries, Booleans; how to perform mathematical operations using Python, and more.

With an interesting mix of theory and practicals, explore Python and its features, and progress from beginner to being skilled in this popular scripting language

As you settle into the Python ecosystem, you'll learn about data structures and study ways to correctly store and represent information. By working through specific examples, you'll learn how Python implements object-oriented programming (OOP) concepts of abstraction, encapsulation of data, inheritance, and polymorphism. You'll be given an overview of how imports, modules, and packages work in Python, how you can handle errors to prevent apps from crashing, as well as file manipulation.

By the end of this book, you'll have built up an impressive portfolio of projects and armed yourself with the skills you need to tackle Python projects in the real world.

Uncover modern Python with this guide to Python data structures, design patterns, and effective object-oriented techniques

Starting with a detailed analysis of object-oriented programming, you will use the Python programming language to clearly grasp key concepts from the object-oriented paradigm. You will learn how to create maintainable applications by studying higher level design patterns. The book will show you the complexities of string and file manipulation, and how Python distinguishes between binary and textual data. Not one, but two very powerful automated testing systems, unittest and pytest, will be introduced in this book. You'll get a comprehensive introduction to Python's concurrent programming ecosystem.

By the end of the book, you will have thoroughly learned object-oriented principles using Python syntax and be able to create robust and reliable programs confidently.

Journey into the world of deep learning using PyTorch. Recognize images, translate languages, and paint unique pictures

Are you ready to go on a journey into the world of deep learning? This course will be your guide through the joys and dangers of this new wave of machine learning. Why? Because, let's face it, getting started with deep learning is difficult. Tasks such as choosing between multiple frameworks, understanding APIs, and debugging code are hard. Is there an another way? Yes. Meet PyTorch. Like Python, PyTorch has a clean and simple API, which makes building neural networks faster and easier. It's also modular, and that makes debugging your code a breeze. This course will be one hell of an adventure into the world of deep learning!

Become a Python programmer in one week. Learn to code in Python from scratch with this hands-on course

We get you started setting up your environment and the tools you need to start programming in Python. You will be learning about variables and operators and how to make use of them in Python programs. You will learn all about control flow statements and loops in Python and you will be using them in your programs to solve your coding problems.

Then you will learn to use Python's advanced data structures such as lists and dictionaries. You will be able to organize in functions and save time coding by writing code that can be reused. Then, you will learn about Python modules and how to make use of them. On the last day, you will start interacting with files using Python code. The course will give you a strong entry point into programming in general and programming in Python in particular.

Deep Learning allows you to solve problems where traditional Machine Learning methods might perform poorly: detecting and extracting objects from images, extracting meaning from text, and predicting outcomes based on complex dependencies, to name a few. In this course you will learn how to use Deep Learning in practice by going through real-world examples.

You will start of by creating neural networks to predict the demand for airline travel in the future. Then, you'll run through a scenario where you have to identify negative tweets for a celebrity by using Convolutional Neural Networks (CNN's). Next you will create a neural network which will be able to identify smiles in your camera app. Finally, the last project will help you forecast a company's stock prices for the next day using Deep Learning.

Master the most popular Machine Learning tools by building your own models to tackle real-world problems

This course will introduce you to tools with which you can build predictive models with Python, the core of a Data Scientist's toolkit. Through some really interesting examples, the course will take you through a variety of challenges: predicting the value of a house in Boston, the batting average of a baseball player, their survival chances had they been on the Titanic, or any other number of other interesting problems.

This course will guide you through the tools in the Python ecosystem that Data Scientists use to get results in a matter of hours - and with practice - in a matter of minutes. The best way to learn is through examples, and this course will guide you through all the steps needed to train and test your models by tackling several classifications and regression challenges.

Learn how to create an impressive trading bot using the different Python tools

This course is a great opportunity to get started with trading, reap the rewards, and take the markets by storm. Programmers who have a basic knowledge of trading in traditional assets and wish to develop their own trading bots will find that this course addresses their core concerns and shows how to go about designing and developing a trading bot.

The course will enable you to get started with creating a traditional asset trading bot. It will arm you with all the necessary programming tools and techniques to develop a full-fledged trading bot that numerous investors/traders can utilize. It covers general features such as using a financial calculator to do conversions, simply by interacting with a bot. Your customers, using your trading, bot can look up recent trends to make informed predictions and see what others have been trading, and how much.

In this course, we introduce, via intuition rather than theory, the core of what makes Machine Learning work. Learn how to use labeled datasets to classify objects or predict future values, so that you can provide more accurate and valuable analysis. Use unlabelled datasets to do segmentation and clustering, so that you can separate a large dataset into sensible groups.

You will learn to understand and estimate the value of your dataset. We guide you through creating the best performance metric for your task at hand, and how that takes you to the correct model to solve your problem. Understand how to clean data for your application, and how to recognize which Machine Learning task you are dealing with.

Fully expanded and upgraded, the latest edition of Python Data Science Essentials will help you succeed in data science operations using the most common Python libraries.

This book offers up-to-date insight into the core of Python, including the latest versions of the Jupyter Notebook, NumPy, pandas, and scikit-learn. The book covers detailed examples and large hybrid datasets to help you grasp essential statistical techniques for data collection, data munging and analysis, visualization, and reporting activities. You will also gain an understanding of advanced data science topics such as machine learning algorithms, distributed computing, tuning predictive models, and natural language processing. Furthermore, You’ll also be introduced to deep learning and gradient boosting solutions such as XGBoost, LightGBM, and CatBoost.

By the end of the book, you will have gained a complete overview of the principal machine learning algorithms, graph analysis techniques, and all the visualization and deployment instruments that make it easier to present your results to an audience of both data science experts and business users.

Use common Python libraries and tools to excel in network and host digital forensics

This course will walk you through digital forensics on network traffic, host analysis, and memory analysis.The course starts with network forensics, an important aspect of any investigation. You will learn to read, sort, and sniff raw packets and also analyze network traffic. These techniques will help you drive your host analysis. You will learn about tools you'll need to perform a complete investigation with the utmost efficiency in both Windows and GNU/Linux environments with Python.

Next, you will learn more advanced topics such as viewing data in PE and ELF binaries. It's vital to analyze volatile memory during an investigation as it provides details about what is actually running on a given system. So, you will learn the best tools to obtain and analyze volatile memory images. Finally, you will learn how to use Python in order to think like an attacker. You will complete enumeration, exploitation, and data exfiltration.

Delve into the fundamentals of the platform: Python, IPython, and the Jupyter Notebook while exploring data analysis tasks on real-world datasets

Python is a user-friendly and powerful programming language. IPython offers a convenient interface to the language and its analysis libraries, while Jupyter Notebook is a rich environment, well-adapted to data science and visualization. Together, these open-source tools are widely used by beginners and experts around the world, and in a huge variety of fields and endeavors.

This course is a beginner-friendly guide to the Python data analysis platform. After an introduction to the Python language, IPython, and Jupyter Notebook, you will learn how to analyze and visualize data on real-world examples, how to create graphical user interfaces for image processing in Notebook, and how to perform fast numerical computations for scientific simulations with NumPy, Numba, Cython, and ipyparallel. By the end of this course, you will be able to perform in-depth analyses of all sorts of data.

Deep learning simplified by taking supervised, unsupervised, and reinforcement learning to the next level using the Python ecosystem

The purpose of this book is two-fold; firstly, we focus on detailed coverage of deep learning (DL) and transfer learning, comparing and contrasting the two with easy-to-follow concepts and examples. The second area of focus is real-world examples and research problems using TensorFlow, Keras, and the Python ecosystem with hands-on examples.

The book starts with the key essential concepts of ML and DL, followed by depiction and coverage of important DL architectures such as convolutional neural networks (CNNs), deep neural networks (DNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), and capsule networks. Our focus then shifts to transfer learning concepts, such as model freezing, fine-tuning, pre-trained models including VGG, inception, ResNet, and how these systems perform better than DL models with practical examples. In the concluding chapters, we will focus on a multitude of real-world case studies and problems associated with areas such as computer vision, audio analysis and natural language processing (NLP).

Exploit various design patterns to master the art of solving problems using Python

This book takes you through a variety of design patterns and explains them with real-world examples. You will get to grips with low-level details and concepts that show you how to write Python code, without focusing on common solutions as enabled in Java and C++. You'll also fnd sections on corrections, best practices, system architecture, and its designing aspects. This book will help you learn the core concepts of design patterns and the way they can be used to resolve software design problems. You'll focus on most of the Gang of Four (GoF) design patterns, which are used to solve everyday problems, and take your skills to the next level with reactive and functional patterns that help you build resilient, scalable, and robust applications.

By the end of the book, you'll be able to effciently address commonly faced problems and develop applications, and also be comfortable working on scalable and maintainable projects of any size.

This book begins with helping you to build your first prediction model using the popular Python library, scikit-learn. You will understand how to build a classifier using an effective machine learning technique, random forest, and decision trees. With exciting projects on predicting bird species, analyzing student performance data, song genre identification, and spam detection, you will learn the fundamentals and various algorithms and techniques that foster the development of these smart applications. In the concluding chapters, you will also understand deep learning and neural network mechanisms through these projects with the help of the Keras library.

By the end of this book, you will be confident in building your own AI projects with Python and be ready to take on more advanced projects as you progress.

Gain an in-depth understanding of data analysis with various Python packages

In this course you will learn all the necessary libraries that make data analytics with Python a joy.You will get into hands-on data analysis and machine learning by coding in Python. You will also learn the Numpy library used for numerical and scientific computation. You will also employ useful libraries for visualization, Matplotlib and Seaborn, to provide insights into data. Further you will learn various steps involved in building an end-to-end machine learning solution. The ease of use and efficiency of these tools will help you learn these topics very quickly. The video course is prepared with applications in mind. You will explore coding on real-life datasets, and implement your knowledge on projects.

By the end of this course, you'll have embarked on a journey from data cleaning and preparation to creating summary tables, from visualization to machine learning and prediction. This video course will prepare you to the world of data science.

Gain an in-depth understanding of data analysis with various Python packages

In this course you will learn all the necessary libraries that make data analytics with Python a joy.You will get into hands-on data analysis and machine learning by coding in Python. You will also learn the Numpy library used for numerical and scientific computation. You will also employ useful libraries for visualization, Matplotlib and Seaborn, to provide insights into data. Further you will learn various steps involved in building an end-to-end machine learning solution. The ease of use and efficiency of these tools will help you learn these topics very quickly. The video course is prepared with applications in mind. You will explore coding on real-life datasets, and implement your knowledge on projects.

By the end of this course, you'll have embarked on a journey from data cleaning and preparation to creating summary tables, from visualization to machine learning and prediction. This video course will prepare you to the world of data science.

Cryptography is essential for protecting sensitive information, but it is often performed inadequately or incorrectly.

Hands-On Cryptography with Python starts by showing you how to encrypt and evaluate your data. The book will then walk you through various data encryption methods,such as obfuscation, hashing, and strong encryption, and will show how you can attack cryptographic systems. You will learn how to create hashes, crack them, and will understand why they are so different from each other. In the concluding chapters, you will use three NIST-recommended systems: the Advanced Encryption Standard (AES), the Secure Hash Algorithm (SHA), and the Rivest-Shamir-Adleman (RSA).

By the end of this book, you will be able to deal with common errors in encryption.

This course is about data structures and algorithms. We are going to implement problems in Python. You will start by learning the basics of data structures, linked lists, and arrays in Python. You will be shown how to code tuples in Python followed by an example that shows how to program dicts and sets in Python. You will learn about the use of pointers in Python. You will then explore linear data structures in Python such as stacks, queues, and hash tables. In these you will learn how to implement a stack and code queues and deques. There will also be a demonstration on how to realize a hash table in Python. Following this you will learn how to use tree/graph data structures including binary trees, heaps and priority queues in Python. You will program priority queues and red-black trees in Python with examples. Finally, you will be shown how to apply different algorithms such as Graph traversal, Shortest Path, Minimum Spanning Tree, Maximum Flow tree, and DAG topological sorting

This course teaches all these concepts in a very practical hands-on approach without burdening you with lots of theory. By the end of the course, you will have learned how to implement various data structures and algorithms in Python.

Get to grips with the most popular Python packages that make Data Analysis possible

Hands-On Data Analysis with NumPy and Pandas starts by guiding you in setting up the right environment for data analysis with Python, along with helping you install the correct Python distribution. In addition to this, you will work with the Jupyter notebook and set up a database. Once you have covered Jupyter, you will dig deep into Python’s NumPy package, a powerful extension with advanced mathematical functions.

You will then move on to creating NumPy arrays and employing different array methods and functions. You will explore Python’s pandas extension which will help you get to grips with data mining and learn to subset your data. Last but not the least you will grasp how to manage your datasets by sorting and ranking them. By the end of this book, you will have learned to index and group your data for sophisticated data analysis and manipulation.

The Python Data Science Essentials video series takes you through all you need to know to succeed in data science using Python. Get insights into the core of Python data, including the latest versions of Jupyter Notebook, NumPy, Pandas and scikit-learn. In this course, you will delve into building your essential Python 3.6 data science toolbox, using a single-source approach that will allow to work with Python 2.7 as well. Get to grips fast with data munging and preprocessing, and prepare for machine learning and visualization techniques.

The course is structured as a data science project. You will always benefit from clear code and simplified examples to help you understand the underlying mechanics and real-world datasets.

Getting started with neural networks in PyTorch - Facebook's great neural network framework

Artificial intelligence (AI) is the hottest topic currently out there, there's no doubt about that. This is the future. Neural networks in particular have seen a lot of attention and they will be used everywhere -self driving cars, predictions in finance and sales forecasts - everywhere and across all industries. To be successful in the working world of tomorrow we have to expose ourselves to this interesting topic - and from the author's personal experience - coding your own neural network is the best way to understand how they work. Besides TensorFlow there is a new very interesting deep learning framework which is developed by Facebook - PyTorch. A challenge? Indeed. But together we can do it. Excited? I hope so. See you in the first lecture. Let's get into it.

The goal of this course is to become familiar with this framework and create your own deep neural networks - a multi-layer perceptron and a convolutional neural network for image classification.

Build data-intensive applications locally and deploy at scale using the combined powers of Python and Spark 2.0

Apache Spark is an open source framework for efficient cluster computing with a strong interface for data parallelism and fault tolerance. This course will show you how to leverage the power of Python and put it to use in the Spark ecosystem. You will start by getting a firm understanding of the Spark 2.0 architecture and how to set up a Python environment for Spark. You will get familiar with the modules available in PySpark.

You will learn how to abstract data with RDDs and DataFrames and understand the streaming capabilities of PySpark. Also, you will get a thorough overview of machine learning capabilities of PySpark using ML and MLlib, graph processing using GraphFrames, and polyglot persistence using Blaze. Finally, you will learn how to deploy your applications to the cloud using the spark-submit command. By the end of this course, you will have established a firm understanding of the Spark Python API and how it can be used to build data-intensive applications.

Data is becoming a force to reckon with. With the amount of data that is being generated every minute, dealing with data has become more important. The importance of data lies in the fact that it allows us to look at our history and predict the future. Data science is the field that deals with collecting, sorting, organizing and also analyzing huge amounts of data. This data is then used to understand the current and future trends. This field borrows techniques and theories from across multiple fields such as mathematics, statistics, computer science, information science, etc. It also aids other domains such as machine learning, data mining, databases and visualization. Data scientists are gaining importance and are also earning higher salaries, which means this is the right time to become a data scientist. While, it might seem easy, sorting data, these scientists are responsible for writing important algorithms and programs to help sort and analyze the data – and this isn’t an easy task.

The course will cover a number of different concepts such as an introduction to data science including concepts such as linear algebra, probability and statistics, Matplotlib, charts and graphs, data analysis, visualization of non uniform data, hypothesis and gradient descent, data clustering and so much more. That’s not all, we’ll also include projects to help you show exactly how to build visuals using Python. You can learn all this and tons of interesting stuff in this unique data science course. Enroll now and start building next generation interfaces for your data.

All the materials are provided including the code files. You achieve two targets with one single course - The complete Python programming language and Selenium WebDriver automation. We start from beginners' level and go through to advanced level. This is a single course for everything you need to know related to Web UI automation.

This practical guide will teach you how deep learning (DL) can be used to solve complex real-world problems.

Recent developments in reinforcement learning (RL), combined with deep learning (DL), have seen unprecedented progress made towards training agents to solve complex problems in a human-like way. Google’s use of algorithms to play and defeat the well-known Atari arcade games has propelled the field to prominence, and researchers are generating new ideas at a rapid pace.

Deep Reinforcement Learning Hands-On is a comprehensive guide to the very latest DL tools and their limitations. You will evaluate methods including Cross-entropy and policy gradients, before applying them to real-world environments. Take on both the Atari set of virtual games and family favorites such as Connect4. The book provides an introduction to the basics of RL, giving you the know-how to code intelligent learning agents to take on a formidable array of practical tasks. Discover how to implement Q-learning on ‘grid world’ environments, teach your agent to buy and trade stocks, and find out how natural language models are driving the boom in chatbots.

Delve into neural networks, implement deep learning algorithms, and explore layers of data abstraction with the help of this comprehensive TensorFlow guide.

Throughout the book, you’ll learn how to implement deep learning algorithms for machine learning systems and integrate them into your product offerings, including search, image recognition, and language processing. Additionally, you’ll learn how to analyze and improve the performance of deep learning models. This can be done by comparing algorithms against benchmarks, along with machine intelligence, to learn from the information and determine ideal behaviors within a specific context.

After finishing the book, you will be familiar with machine learning techniques, in particular the use of TensorFlow for deep learning, and will be ready to apply your knowledge to research or commercial projects.

This course lays the foundation from which you can begin using Python to solve any problem - whether in data analysis, machine learning or web development. It gives you a fundamental understanding of Python loops, data structures, functions, classes, and more, to help you solve basic programming tasks so that you can confidently apply those skills to solve real problems. The course assumes zero prior experience with Python, though some fundamental knowledge of programming is recommended.

The books on this page are all general introductions to the Python language. Most of these books will contain a few chapters on particular applications such as GUI interfaces or Web programming, but won't go into great detail on any one topic; refer to the PythonBooks page for lists of application-specific books. Experienced programmers who prefer a brief and condensed introduction should look at the list of ReferenceBooks.

This course will start by introducing the modules and the tools we will be using and how to set up a python environment to perform automation tasks and to deal with file editors and IDE like Pycharm. The course will cover network automation tasks and administration tasks with Python Fabric to automate the execution of web server with simple python programs. Moving ahead, you will learn to create database servers with python and backup them. Also, you will be creating users on multiple servers to manage users and then check the health of the Enterprise and then you will be performing automation tasks on the cloud infrastructure with python. The course will make the most of Python libraries and modules to automate your infrastructure. Leverage Python programming to automate server configuration and administration tasks.

By the end of the course, you will be able to efficiently develop your python skills making it an alternate automation tool from the major automation frameworks.The use cases in this course will help you to track the processes with high utilization on all servers and create web server via python code. So, now need not depend on Network Automation Tools like a puppet, Ansible, and chef and grab this course to make your daily work easy with automation and python modules which will help you to deliver the service more faster than before.

Learn to visualize data for real-world solutions using Python and its popular libraries for effective data analysis.

The video course introduces visualization concepts so viewers can analyze large and small sets of data using libraries such as Matplotlib, IPython, and so on.

This course primarily employs the IPython environment and matplotlib, with the following structure:

Introduce key data visualization libraries (matplotlib and so on.) and cover data importing/exporting (CSV, Excel, JSON and so on). Introduce real-world data sets (to be visualized in the video). Visualization types/techniques (bar chart, histogram, scatter plot, geospatial, and so on); demonstrate how to customize visualizations. Introduce intermediate topics to create more advanced visualizations and using complex techniques, such as real-time data visualization. By the end of the course, you will be able to demonstrate visualizations with interesting, real-world data sets.

A definitive guide to learn Python 3.x with examples and exercises, created with beginners in mind

This is the most comprehensive yet simple course on the Python programming language and it concentrates on Python 3.x. This means that what you will learn is relevant, not obsolete. No prior coding experience is needed. Python is one of the most useful programming languages to learn. You can use it for the back-end of web applications, games, in-house scripts, and even for building robust test automation frameworks.

In recent years, the demand for Python has exploded in the job market with insufficient developers to fill the available roles. Additionally, the QA industry is rapidly transitioning to Python and building automation tools.

This book will help you master the art of GUI programming. It delivers the bigger picture of GUI programming by building real-world, productive, and fun applications such as a text editor, drum machine, game of chess, audio player, drawing application, piano tutor, chat application, screen saver, port scanner, and much more. In every project, you will build on the skills acquired in the previous project and gain more expertise. You will learn to write multithreaded programs, network programs, database-driven programs, asyncio based programming and more. You will also get to know the modern best practices involved in writing GUI apps.

With its rich source of sample code, you can build upon the knowledge gained with this book and use it in your own projects in the discipline of your choice.

Discover solutions to all your Tkinter and Python GUI development problems

As one of the more versatile programming languages, Python is well-known for its batteries-included philosophy, which includes a rich set of modules in its standard library; Tkinter is the library included for building desktop applications. Due to this, Tkinter is a common choice for rapid GUI development, and more complex applications can benefit from the full capabilities of this library. This book covers all of your Tkinter and Python GUI development problems and solutions.

Tkinter GUI Application Development Cookbook starts with an overview of Tkinter classes and at the same time provides recipes for basic topics, such as layout patterns and event handling. Next, we cover how to develop common GUI patterns, such as entering and saving data, navigating through menus and dialogs, and performing long-running actions in the background.You can then make your apps leverage network resources effectively and perform graphical operations on a canvas and related tasks such as detecting collisions between items. Finally, this book covers using themed widgets, an extension of Tk widgets that have a more native look and feel. Finally, this book covers using the canvas and themed widgets.

Build strong foundation for entering the world of Machine Learning and data science with the help of this comprehensive guide

As the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine Learning applications are everywhere, from self-driving cars, spam detection, document searches, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of big data and data science. The main challenge is how to transform data into actionable knowledge.

In this course you will learn all the important Machine Learning algorithms that are commonly used in the field of data science. These algorithms can be used for supervised as well as unsupervised learning, reinforcement learning, and semi-supervised learning. A few famous algorithms that are covered in this book are: Linear regression, Logistic Regression, SVM, Naive Bayes, K-Means, Random Forest, and Feature engineering. In this course, you will also learn how these algorithms work and their practical implementation to resolve your problems.

Mike Driscoll takes you on a journey talking to a hall-of-fame list of truly remarkable Python experts. You'll be inspired every time by their passion for the Python language, as they share with you their experiences, contributions, and careers in Python.

Hear from these key Python thinkers about the current status of Python, and where it's heading in the future. Listen to their close thoughts on significant Python topics, such as Python's role in scientific computing, and machine learning. Understand the direction of Python, and what needs to change for Python 4

Each of these twenty Python Interviews can inspire and refresh your relationship with Python and the people who make Python what it is today. Let these interviews spark your own creativity, and discover how you also have the ability to make your mark on a thriving tech community. This book invites you to immerse in the Python landscape, and let these remarkable programmers show you how you too can connect and share with Python programmers around the world. Learn from their opinions, enjoy their stories, and use their tech tips.

Build a strong programming foundation with the popular Python language

This course teaches the viewer Python in an engaging, friendly, example-driven way. In this course, we cover computer programming using Python. We start by running a "Hello World" program, followed by the discussion of fundamentals, such as common data structures, working with strings, and program flow controls. Later, we focus on writing modular and reusable code, using functions.

After getting familiar with the basic concepts, we delve further by covering Object Oriented Programming, errors and exception handling, and working with files. We also discuss Python standard libraries and external libraries. Finally, we provide future directions and next steps for avid learners keen to take their skills to the next level.

By the end of this course the audience will be able to write Python programs and scripts that perform most of their daily tasks—including reading a list of strings, separating values by a specific delimiter, removing duplicates, and more. By the end of the course, the viewer should be able to do everything expected of a novice Python programmer.

Scikit-learn has evolved as a robust library for machine learning applications in Python with support for a wide range of supervised and unsupervised learning algorithms.

This course begins by taking you through videos on linear models; with scikit-learn, you will take a machine learning approach to linear regression. As you progress, you will explore logistic regression. Then you will build models with distance metrics, including clustering. You will also look at cross-validation and post-model workflows, where you will see how to select a model that predicts well. Finally, you'll work with Support Vector Machines to get a rough idea of how SVMs work, and also learn about the radial basis function (RBF) kernel.

Build a strong foundation to enter the world of Machine Learning and data science with the help of this comprehensive guide

As the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine Learning applications are everywhere, from self-driving cars to spam detection, document search, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of Big Data and Data Science. The main challenge is how to transform data into actionable knowledge.

In this course, you’ll be introduced to the Natural Processing Language and Recommendation Systems, which help you run multiple algorithms simultaneously. Also, you’ll learn about Deep learning and TensorFlow. Finally, you’ll see how to create an Ml architecture.

Learn the techniques for object recognition, 3D reconstruction, stereo imaging, and other computer vision applications using examples on different functions of OpenCV.

We start off by manipulating images using simple filtering and geometric transformations. We then discuss affine and projective transformations and see how we can use them to apply cool advanced manipulations to your photos like resizing them while keeping the content intact or smoothly removing undesired elements. We will then cover techniques of object tracking, body part recognition, and object recognition using advanced techniques of machine learning such as artificial neural network. 3D reconstruction and augmented reality techniques are also included. The book covers popular OpenCV libraries with the help of examples.

This book is a practical tutorial that covers various examples at different levels, teaching you about the different functions of OpenCV and their actual implementation. By the end of this book, you will have acquired the skills to use OpenCV and Python to develop real-world computer vision applications.

Take your R, Python, and Tableau Data Visualization Skills from Rookie to Pro!

In this course, we’ll be using tools such as Tableau, which is the best visualization tool as ranked by the Gartner Report 2017, as well as open source tools such as R and Python to understand data and share findings between fellow data scientists.

This course takes you gradually from basic, fundamental topics to advanced topics with a ton of examples. It also focuses on making you independent of these tools, so you can carry forward your visualization knowledge to any other tool you may need to use. We cover three tools in visualization, namely R, Python, and Tableau and setting up your basic framework for any project you take up. By the end of this course, you’ll have the fundamentals in place, so you can make visualizations on your own with the correct use of various visualization elements.

The course is an introduction to the basics of deep learning methods. We will start with object detection and tracking, in which we will track faces, objects and eyes. We will then build a neural network and an OCR. We will then learn how to build learning agents that can learn from interacting with the environment. We will use Deep Learning with Convolutional Neural Networks, and use TensorFlow to build neural networks. We will then build an image classifier using convolutional neural networks.

Explore the world of sequence learning in Python-text, speech, and more

Enter and explore the fascinating world of intelligent apps with Artificial Intelligence with Python. Artificial Intelligence is becoming increasingly relevant in the modern world. By harnessing the power of algorithms, you can create apps that intelligently interact with the world around you, automatic speech recognition systems, and more.

Explore the concepts and techniques of AI and bring its magical power to your applications

This course is a go-to guide for the four topics, logic programming, heuristic search, genetic algorithms and building games with AI. It will help you learn to programme with AI. The course will start with the basic puzzles, parsing trees and expression matching. This will be followed by building solutions for region coloring and maze solving. The course also has fun-filled videos on building bots to play Tic-tac-toe, Connect Four and Hexapawn.

Build real-world Artificial Intelligence (AI) applications to intelligently interact with the world around you, explore real-world scenarios, and learn about the various algorithms that can be used to build AI applications. Packed with insightful examples and topics such as predictive analytics and deep learning, this course is a must-have for Python developers.

Learn to use scikit-learn operations and functions for Machine Learning and deep learning applications.

This book includes walk throughs and solutions to the common as well as the not-so-common problems in machine learning, and how scikit-learn can be leveraged to perform various machine learning tasks effectively.

Over 95 hands-on recipes to leverage the power of pandas for efficient scientific computation and data analysis

This book will provide you with unique, idiomatic, and fun recipes for both fundamental and advanced data manipulation tasks with pandas. Some recipes focus on achieving a deeper understanding of basic principles, or comparing and contrasting two similar operations. Other recipes will dive deep into a particular dataset, uncovering new and unexpected insights along the way.

Learn how to use Pandas for Predictive Analysis by employing scikit-learn

In this course you learn that stand alone data analysis is fine but what most companies these days are looking for is to do Predictive analysis using their data. This advanced course, will make you ready to start doing Predictive Analysis on your data by showing you how to build Machine Learning models with scikit-learn and pandas.

Businesses today are evolving so rapidly that having their own infrastructure to support their expansion is not feasible. As a result, they have been resorting to the elasticity of the cloud to provide a platform to build and deploy their highly scalable applications.This video will be the one stop for you to learn all about building cloud-native architectures in Python. It will begin by introducing you to cloud-native architecture and will help break it down for you. Then you’ll learn how to build microservices in Python using REST APIs in an event-driven approach and you will build the web layer.

Solve different problems in modelling deep neural networks using Python, Tensorflow, and Keras with this practical guide

This book provides a top-down and bottom-up approach to demonstrate deep learning solutions to real-world problems in different areas. These applications include Computer Vision, Natural Language Processing, Time Series, and Robotics.

The Python Deep Learning Cookbook presents technical solutions to the issues presented, along with a detailed explanation of the solutions. Furthermore, a discussion on corresponding pros and cons of implementing the proposed solution using one of the popular frameworks like TensorFlow, PyTorch, Keras and CNTK is provided. The book includes recipes that are related to the basic concepts of neural networks. All techniques s, as well as classical networks topologies. The main purpose of this book is to provide Python programmers a detailed list of recipes to apply deep learning to common and not-so-common scenarios.

Over 60 recipes to help you learn digital forensics and leverage Python scripts to amplify your examinations.

By leveraging the Python recipes explored throughout this book, you make the complex simple, quickly extracting relevant information from large datasets. You will explore, develop, and deploy Python code and libraries to provide meaningful results that can be immediately applied to your investigations. Throughout the Python Digital Forensics Cookbook, recipes include topics such as working with forensic evidence containers, parsing mobile and desktop operating system artifacts, extracting embedded metadata from documents and executables, and identifying indicators of compromise. You will also learn to integrate scripts with Application Program Interfaces (APIs) such as VirusTotal and PassiveTotal, and tools such as Axiom, Cellebrite, and EnCase.

By the end of the book, you will have a sound understanding of Python and how you can use it to process artifacts in your investigations.

Put your Python skills to the test and enter the big world of data science to learn the most effective machine learning tools and techniques with this interesting guide.

This video is your entry point to machine learning. It starts with an introduction to machine learning and the Python language and shows you how to complete the necessary setup. Moving ahead, you will learn all the important concepts such as exploratory data analysis, data preprocessing, feature extraction, data visualization and clustering, classification, regression, and model performance evaluation. With the help of the various projects included, you will acquire the mechanics of several important machine learning algorithms, which will no longer seem obscure. Also, you will be guided step-by-step to build your own models from scratch. Toward the end, you will gather a broad picture of the machine learning ecosystem and master best practices for applying machine learning techniques. Throughout this course, you will learn to tackle data-driven problems and implement your solutions with the powerful yet simple Python language.

A down-to-earth, shy but confident take on machine learning techniques that you can put to work today.

This course is very visual: most of the techniques are explained with the help of animations to help you understand better. This course is practical as well: There are hundreds of lines of source code with comments that can be used directly to implement natural language processing and machine learning for text summarization, text classification in Python. The course is also quirky. The examples are irreverent. Lots of little touches: repetition, zooming out so we remember the big picture, active learning with plenty of quizzes. There’s also a peppy soundtrack, and art - all shown by studies to improve cognition and recall.

The book starts with descriptive analysis to create insightful visualizations of internal structures such as trend, seasonality and autocorrelation. Next, the statistical methods of dealing with autocorrelation and non-stationary time series are described. This is followed by exponential smoothing to produce meaningful insights from noisy time series data. At this point, you shift focus towards predictive analysis and introduce autoregressive models such as ARMA and ARIMA for time series forecasting. Later, powerful deep learning methods are presented, to develop accurate forecasting models for complex time series, and under the availability of little domain knowledge. All the topics are illustrated with real-life problem scenarios and their solutions by best-practice implementations in Python.

The book concludes with the Appendix, with a brief discussion of programming and solving data science problems using Python.

A penetration tester who only knows how to use tools written by others is limited to old techniques. Knowledge of a programming language will make you much more powerful. Python is the favorite choice for penetration testers because it combines simplicity and ease of use with advanced features.

The goal of this video course is to show you how you can quickly and easily make simple attack tools with Python, even if you have never programmed before.

Unleash the power of computer vision with Python to carry out image processing and computer vision techniques

This book is a thorough guide for developers who want to get started with building computer vision applications using Python 3. The book is divided into five sections: The Fundamentals of Image Processing, Applied Computer Vision, Making Applications Smarter,Extending your Capabilities using OpenCV, and Getting Hands on. Throughout this book, three image processing libraries Pillow, Scikit-Image, and OpenCV will be used to implement different computer vision algorithms.

This book introduces some of the most popular libraries and frameworks and goes in-depth into how you can leverage these libraries for your own high-concurrent, highly-performant Python programs. We'll cover the fundamental concepts of concurrency needed to be able to write your own concurrent and parallel software systems in Python.

The book will guide you down the path to mastering Python concurrency, giving you all the necessary hardware and theoretical knowledge. We'll cover concepts such as debugging and exception handling as well as some of the most popular libraries and frameworks that allow you to create event-driven and reactive systems.

This video will show you how you can get the most out of Pandas for data analysis.The course starts with teaching you the absolute basics such as installing and setting up of the pandas library. Then, you will be introduced to fundamental data structures in pandas and the different data types, indexing, and more. You will then implement the basic functionalities of the pandas library such as working with different kinds of data, indexing, and handling missing data. The course will also teach you how to analyze and model your data, and organize the results of your analysis in the form of plots or other visualization means. Throughout the course, you will implement simple yet highly effective examples and use-cases which are relevant in the real-world scenario, as you build on your understanding of pandas.By the end of this course, you will have a firm understanding of the basics of pandas. You will be ready to start using pandas for different data science tasks with confidence.

What you will learn:

Create the GUI Form and add widgets

Know how to read different kinds of data into Pandas Dataframes for data analysis.

Attain knowledge of how to manipulate, transform and apply formulas on the data imported into the pandas dataframes

Be trained in analyzing and visualizing different kinds of data using Pandas to gain real world insights.

Get to know how to use Pandas to make predictions using Machine Learning and scikit-learn

Practice ways in which to work with Big Data using Pandas

Imbibe how to work with quantitative financial data and how to model time-series data,

Before you master Python, you need to learn the culture and the tools to become a productive member of any Python project. This book will give you a practical and thorough introduction to Python programming, providing you with the insight and technical craftsmanship you need to be a productive member of any Python project. As a Python developer, know the tools, basic idioms and of course the ins and outs of the language, the standard library and other modules to be able to jump into most projects.

With this book you will learn how to use pandas to perform data analysis in Python. You will start with an overview of data analysis and iteratively progress from modeling data, to accessing data from remote sources, performing numeric and statistical analysis, through indexing and performing aggregate analysis, and finally to visualizing statistical data and applying pandas to finance.

With the knowledge you gain from this book, you will quickly learn pandas and how it can empower you in the exciting world of data manipulation, analysis and science.

What you will learn:

Understand how data analysts and scientists think about of the processes of gathering and understanding data

Learn how pandas can be used to support the end-to-end process of data analysis

Use pandas Series and DataFrame objects to represent single and multivariate data

Slicing and dicing data with pandas, as well as combining, grouping, and aggregating data from multiple sources

How to access data from external sources such as files, databases, and web services

Represent and manipulate time-series data and the many of the intricacies involved with this type of data

How to visualize statistical information

How to use pandas to solve several common data representation and analysis problems within finance

Python is a great language to get started in the world of programming and application development. This book will help you to take your skills to the next level having a good knowledge of the fundamentals of Python.

Begin with the absolute foundation, covering the basic syntax, type variables and operators. Then you can move on to concepts like statements, arrays, operators, string processing and I/O handling. You’ll be able to learn how to operate tuples and understand the functions and methods of lists. Develop a deep understanding of list and tuples and learn python dictionary. As you progress through the book, you’ll learn about function parameters and how to use control statements with the loop. You’ll further learn how to create modules and packages, storing of data as well as handling errors. Later, dive into advanced level concepts such as Python collections and how to use class, methods, objects in Python.

By the end of this book, you will be able to take your skills to the next level having a good knowledge of the fundamentals of Python.

What you will learn:

Use if else statement with loops and how to break, skip the loop

Get acquainted with python types and its operators

Create modules and packages

Learn slicing, indexing and string methods

Explore advanced concepts like collections, class and objects

Learn dictionary operation and methods

Discover the scope and function of variables with arguments and return value

This book will guide you through the very basics of creating a fully functional GUI in Python with only a few lines of code. Each and every recipe adds more widgets to the GUIs we are creating. While the cookbook recipes all stand on their own, there is a common theme running through all of them.

What you will learn:

Create the GUI Form and add widgets

Arrange the widgets using layout managers

Use object-oriented programming to create GUIs

Create Matplotlib charts

Use threads and talking to networks

Talk to a MySQL database via the GUI

Perform unit-testing and internationalizing the GUI

Extend the GUI with third-party graphical libraries

Get to know the best practices to create GUIs

Who this book is written for:

This book is for intermediate Python programmers who wish to enhance their Python skills by writing powerful GUIs in Python. As Python is such a great and easy to learn language, this book is also ideal for any developer with experience of other languages and enthusiasm to expand their horizon.

In this book, you will be able to understand the power of linked lists, double linked lists, and circular linked lists. You will be able to create complex data structures such as graphs, stacks and queues. We will explore the application of binary searches and binary search trees. You will learn the common techniques and structures used in tasks such as preprocessing, modeling, and transforming data.

What you will learn:

Gain a solid understanding of Python data structures.

Build sophisticated data applications.

Understand the common programming patterns and algorithms used in Python data science.

Write efficient robust code.

Who this book is written for:

This book will appeal to all Python developers. A basic knowledge of Python is expected.

This book will get you started with Python data analysis by introducing the basics of data analysis and supported Python libraries such as matplotlib, NumPy, and pandas. Create visualizations by choosing color maps, different shapes, sizes, and palettes then delve into statistical data analysis using distribution algorithms and correlations.

What you will learn:

Understand the importance of data analysis and master its processing steps

Get comfortable using Python and its associated data analysis libraries such as Pandas, NumPy, and SciPy

Perform web scraping and work with different databases, Hadoop, and Spark

Use statistical models to discover patterns in data

Detect similarities and differences in data with clustering

Work with Jupyter Notebook to produce publication-ready figures to be included in reports

Who this book is written for:

This course is for developers, analysts, and data scientists who want to learn data analysis from scratch. This course will provide you with a solid foundation from which to analyze data with varying complexity. A working knowledge of Python (and a strong interest in playing with your data) is recommended.

This book starts with an introduction to machine learning and the Python language and shows you how to complete the setup. Moving ahead, you will learn all the important concepts such as, exploratory data analysis, data preprocessing, feature extraction, data visualization and clustering, classification, regression and model performance evaluation.

What you will learn:

Exploit the power of Python to handle data extraction, manipulation, and exploration techniques

Use Python to visualize data spread across multiple dimensions and extract useful features

The book will give you all the practical information available on the subject, including the best practices, using real-world use cases. You will learn to recognize and extract information to increase predictive accuracy and optimize results.

What you will learn:

Get a practical deep dive into deep learning algorithms

Explore deep learning further with Theano, Caffe, Keras, and TensorFlow

Learn about two of the most powerful techniques at the core of many practical deep learning implementations: Auto-Encoders and Restricted Boltzmann Machines

Get to know device strategies so you can use deep learning algorithms and libraries in the real world

Who this book is written for:

This book is for Data Science practitioners as well as aspirants who have a basic foundational understanding of Machine Learning concepts and some programming experience with Python. A mathematical background with a conceptual understanding of calculus and statistics is also desired.

This book starts off by explaining how Python fits into an application architecture. As you move along, you will understand the architecturally significant demands and how to determine them. Later, you’ll get a complete understanding of the different architectural quality requirements that help an architect to build a product that satisfies business needs, such as maintainability/reusability, testability, scalability, performance, usability, and security.

What you will learn:

Build programs with the right architectural attributes

Use Enterprise Architectural Patterns to solve scalable problems on the Web

Understand design patterns from a Python perspective

Optimize the performance testing tools in Python

Deploy code in remote environments or on the Cloud using Python

Secure architecture applications in Python

Who this book is written for:

This book is for experienced Python developers who are aspiring to become the architects of enterprise-grade applications or software architects who would like to leverage Python to create effective blueprints of applications.

This book teaches you to design and develop data mining applications using a variety of datasets, starting with basic classification and affinity analysis. This book covers a large number of libraries available in Python, including the Jupyter Notebook, pandas, scikit-learn, and NLTK.

What you will learn:

Apply data mining concepts to real-world problems

Predict the outcome of sports matches based on past results

Determine the author of a document based on their writing style

Use APIs to download datasets from social media and other online services

Find and extract good features from difficult datasets

Create models that solve real-world problems

Design and develop data mining applications using a variety of datasets

If you are a Python programmer who wants to get started with data mining, then this book is for you. If you are a data analyst who wants to leverage the power of Python to perform data mining efficiently, this book will also help you. No previous experience with data mining is expected.

With this book, you will learn how to process and manipulate data with Python for complex analysis and modeling. We learn data manipulations such as aggregating, concatenating, appending, cleaning, and handling missing values, with NumPy and Pandas. The book covers how to store and retrieve data from various data sources such as SQL and NoSQL, CSV fies, and HDF5. We learn how to visualize data using visualization libraries, along with advanced topics such as signal processing, time series, textual data analysis, machine learning, and social media analysis.

Retrieve and store your data from RDBMS, NoSQL, and distributed filesystems such as HDFS and HDF5

Visualize your data with open source libraries such as matplotlib, bokeh, and plotly

Learn about various machine learning methods such as supervised, unsupervised, probabilistic, and Bayesian

Understand signal processing and time series data analysis

Get to grips with graph processing and social network analysis

Who this book is written for:

This book is for programmers, scientists, and engineers who have the knowledge of Python and know the basics of data science. It is for those who wish to learn different data analysis methods using Python 3.5 and its libraries. This book contains all the basic ingredients you need to become an expert data analyst.

Data analysis is the process of applying logical and analytical reasoning in order to study each data component. Python's powerful standard libraries or toolkits, such as Pylearn2 and Hebel, offer a fast, reliable, cross-platform environment for data analytics. This learning path will start with basic data analysis and you will then will gradually move on to increasingly complex problems; your journey will end with representing analyzed data in meaningful visualizations.

What you will learn:

Get acquainted with NumPy and use arrays and array-oriented computing in data analysis

Starting from the basics of Artificial Intelligence, you will learn how to develop various building blocks using different data mining techniques. You will see how to implement different algorithms to get the best possible results, and will understand how to apply them to real-world scenarios. If you want to add an intelligence layer to any application that’s based on images, text, stock market, or some other form of data, this exciting book on Artificial Intelligence will definitely be your guide!

What you will learn:

Realize different classification and regression techniques

Understand the concept of clustering and how to use it to automatically segment data

See how to build an intelligent recommender system

Understand logic programming and how to use it

Build automatic speech recognition systems

Understand the basics of heuristic search and genetic programming

Develop games using Artificial Intelligence

Learn how reinforcement learning works

Discover how to build intelligent applications centered on images, text, and time series data

See how to use deep learning algorithms and build applications based on it

Who this book is written for:

This book is for Python developers who want to build real-world Artificial Intelligence applications. This book is friendly to Python beginners, but being familiar with Python would be useful to play around with the code. It will also be useful for experienced Python programmers who are looking to use Artificial Intelligence techniques in their existing technology stacks.

This video gives you access to the world of predictive analytics and demonstrates why Python is one of the world’s leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science courseis invaluable. It coversa wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and featuresguidance and tips on everything from sentiment analysis to neural networks. With this video,you’ll soon be able to answer some of the most important questions facing you and your organization.

What you will learn:

Discover the different types of machine learning and know when to use them

If you want to find out how to use Python to start answering critical questions using your data, this video is ideal. Whether you want to get started from scratch or want to extend your data science knowledge, this is an essential resource.

This video comes with over 100 recipes on the latest version of Python. The recipes will benefit everyone ranging from beginner to an expert. The video is broken down into 13 sections that build from simple language concepts to more complex applications of the language.The recipes will touch upon all the necessary Python concepts related to data structures, OOP, functional programming, as well as statistical programming. You will get acquainted with the nuances of Python syntax and how to effectively use the advantages that it offers. You will end the book equipped with the knowledge of testing, web services, and configuration and application integration tips and tricks.

What you will learn:

See the intricate details of the Python syntax and how to use it to your advantage

SImprove your code readability through functions in Python

SManipulate data effectively using built-in data structures

SGet acquainted with advanced programming techniques in Python

SEquip yourself with functional and statistical programming features

SWrite proper tests to be sure a program works as advertised

SIntegrate application software using Python

Who this video is for:

The video is for web developers, programmers, enterprise programmers, engineers, big data scientist, and so on. If you are a beginner, Modern Python Recipes will get you started. If you are experienced, it will expand your knowledge base. A basic knowledge of programming would help.

The book starts with an introduction to recommendation systems and its applications. You will then start building recommendation engines straight away from the very basics. As you move along, you will learn to build recommender systems with popular frameworks such as R, Python, Spark, Neo4j, and Hadoop. You will get an insight into the pros and cons of each recommendation engine and when to use which recommendation to ensure each pick is the one that suits you the best.

What you will learn:

Build your first recommendation engine

Discover the tools needed to build recommendation engines

Dive into the various techniques of recommender systems such as collaborative, content-based, and cross-recommendations

Create efficient decision-making systems that will ease your work

Familiarize yourself with machine learning algorithms in different frameworks

Master different versions of recommendation engines from practical code examples

Explore various recommender systems and implement them in popular techniques with R,

The purpose of this book is to teach the main concepts of Bayesian data analysis. You will learn how to effectively use PyMC3, a Python library for probabilistic programming, to perform Bayesian parameter estimation, to check models and validate them. This book begins presenting the key concepts of the Bayesian framework and the main advantages of this approach from a practical point of view. Moving on, you will explore the power and flexibility of generalized linear models and how to adapt them to a wide array of problems, including regression and classification. You will also look into mixture models and clustering data, and you will finish with advanced topics like non-parametrics models and Gaussian processes. With the help of Python and PyMC3 you will learn to implement, check and expand Bayesian models to solve data analysis problems.

Understand the essentials Bayesian concepts from a practical point of view

Learn how to build probabilistic models using the Python library PyMC3

Acquire the skills to sanity-check your models and modify them if necessary

Add structure to your models and get the advantages of hierarchical models

Find out how different models can be used to answer different data analysis questions

Learn how to think probabilistically and unleash the power and flexibility of the Bayesian framework

Who this book is written for:

Students, researchers and data scientists who wish to learn Bayesian data analysis with Python and implement probabilistic models in their day to day projects. Programming experience with Python is essential. No previous statistical knowledge is assumed.

In the first module, Python Machine Learning Cookbook, you will learn how to perform various machine learning tasks using a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms.

The second module, Advanced Machine Learning with Python, is designed to take you on a guided tour of the most relevant and powerful machine learning techniques and you’ll acquire a broad set of powerful skills in the area of feature selection and feature engineering.

The third module in this learning path, Large Scale Machine Learning with Python, dives into scalable machine learning and the three forms of scalability. It covers the most effective machine learning techniques on a map reduce framework in Hadoop and Spark in Python.

This Learning Path will teach you Python machine learning for the real world. The machine learning techniques covered in this Learning Path are at the forefront of commercial practice.

This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products:

Work with modern state-of-the-art large-scale machine learning techniques

Learn to use Python code to implement a range of machine learning algorithms and techniques

Who this course is for:

This Learning Path is for Python programmers who are looking to use machine learning algorithms to create real-world applications. It is ideal for Python professionals who want to work with large and complex datasets and Python developers and analysts or data scientists who are looking to add to their existing skills by accessing some of the most powerful recent trends in data science. Experience with Python, Jupyter Notebooks, and command-line execution together with a good level of mathematical knowledge to understand the concepts is expected. Machine learning basic knowledge is also expected.

This video will help you take your first steps in the world of data science, and empower you to conduct data analysis and perform efficient machine learning using Python. Gain value from your data using the various data mining and data analysis techniques in Python, and develop efficient predictive models to predict future results. You will also learn how to perform large-scale machine learning on Big Data using Apache Spark. You don’t have to be an expert coder in Python to get the most out of this video – just a basic programming knowledge of Python is sufficient.

Visualize the results of your analysis using Python’s Matplotlib library

Who this video is for:

If you are a budding data scientist or a data analyst who wants to analyze and gain actionable insights from data using Python, this video is for you. Programmers with some experience in Python who want to enter the lucrative world of Data Science will also find this video to be very useful.

This book offers you the perfect place to lay the foundation for your work in the world of Machine Learning, providing the basic understanding, knowledge, and skills that you can build on with experience and time.

Discover the various forms Machine Learning, and take that which will benefit you the most

Pick up the Python tools you need to know: from pandas to scikit-learn

Learn the relationship between machine learning and big data

Understand and identify potential real-world scenarios where machine learning can be applied

Lay the foundations to get started in the wide world of machine learning

This book goes deeper than simply showing you how to build a Python app, giving you the fundamentals of Python programming that every developer needs to know to make the most of the language. Packed with tutorials and examples this title features everything from data structures, writing reusable code, testing, paradigms, and how Python can be adapted. This free eBook will help transform you from a complete beginner to someone ready to bring the best out of their projects.

Get Python up and running on Windows, Mac, and Linux in no time

Grasp the fundamental concepts of coding, along with the basics of data structures and control flow.

Write elegant, reusable, and efficient code in any situation

Understand when to use the functional or the object oriented programming approach

Learn to be independent, capable of fetching any resource you need, as well as dig deeper

Who this book is written for:

This book is meant for programmers who wants to learn Python programming from a basic to an expert level. The book is mostly self-contained and introduces Python programming to a new reader and can help him become an expert in this trade.

When you're ready to learn to program, Python is a great language to start with. It has a straightforward syntax, you'll find it easy to learn, and it's extremely versatile. Once you master the basics, you'll appreciate Python's very strong online community where you can continue learning and tinkering with more advanced programming techniques. All you need is some help to get going. Learning to program with Python doesn't have to be difficult, in fact it can even be fun. This book will get you started!

Learn Python is an introduction to programming using one of the most popular programming languages. The first few chapters give you a quick background to programming concepts and a hands-on guide to setting up your programming environment. You will then be gently introduced to the basics of programming, building up to writing your first programs! This easy-to-follow tutorial is full of exercises to practice and reinforce each new concept, as well as to give you confidence that you are ready to move on to the next lesson. As you progress, you'll learn programming topics and concepts that are ubiquitous across almost all other programming languages. By the end of the book, you'll have a solid grasp of how to write programs as well as programming best practices.

Who this book is written for:

Anyone without programming experience who wants an easy-to-follow book that guides you through short lessons and exercises.

This third revision of Manning's popular The Quick Python Book offers a clear, crisp updated introduction to the elegant Python programming language and its famously easy-to-read syntax. Written for programmers new to Python, this latest edition includes new exercises throughout. It covers features common to other languages concisely, while introducing Python's comprehensive standard functions library and unique features in detail.

After exploring Python's syntax, control flow, and basic data structures, the book shows how to create and deploy full applications and larger code libraries. It addresses established Python features as well as the advanced object-oriented options available in Python 3. Along the way, you'll survey the current Python development landscape, including Pythonic best practices, data extraction and cleaning, database access, and web frameworks.

Who this book is written for:

This book is for someone who knows how to program, who whats to learn Python quickly and efficiently.

Machine learning is becoming increasingly pervasive in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more. With this book, you will learn how to perform various machine learning tasks in different environments. We’ll start by exploring a range of real-life scenarios where machine learning can be used, and look at various building blocks. Throughout the book, you’ll use a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms. You’ll discover how to deal with various types of data and explore the differences between machine learning paradigms such as supervised and unsupervised learning. We also cover a range of regression techniques, classification algorithms, predictive modeling, data visualization techniques, recommendation engines, and more with the help of real-world examples.

Explore classification algorithms and apply them to the income bracket estimation problem

Use predictive modeling and apply it to real-world problems

Understand how to perform market segmentation using unsupervised learning

Understand how to interact with text data and build models to analyze it

Work with speech data and recognize spoken words using Hidden Markov Models

Analyze stock market data using Conditional Random Fields

Work with image data and build systems for image recognition and biometric face recognition

Grasp how to use deep neural networks to build an optical character recognition system

Who this book is written for:

This book is for Python programmers who are looking to use machine-learning algorithms to create real-world applications. This book is friendly to Python beginners, but familiarity with Python programming would certainly be useful to play around with the code.

This book will help readers develop readable, reliable, and maintainable programs in Python. Starting with an introduction to the concept of modules and packages, this book shows how you can use these building blocks to organize a complex program into logical parts and make sure those parts are working correctly together. Using clearly written, real-world examples, this book demonstrates how you can use modular techniques to build better programs. A number of common modular programming patterns are covered, including divide-and-conquer, abstraction, encapsulation, wrappers and extensibility. You will also learn how to test your modules and packages, how to prepare your code for sharing with other people, and how to publish your modules and packages on GitHub and the Python Package Index so that other people can use them. Finally, you will learn how to use modular design techniques to be a more effective programmer.

Learn how to use modules and packages to organize your Python code

Understand how to use the import statement to load modules and packages into your program

Use common module patterns such as abstraction and encapsulation to write better programs

Discover how to create self-testing Python packages

Create reusable modules that other programmers can use

Learn how to use GitHub and the Python Package Index to share your code with other people

Make use of modules and packages that others have written

Use modular techniques to build robust systems that can handle complexity and changing requirements over time

Who this book is written for:

This book is intended for beginner to intermediate level Python programmers who wish to learn how to use modules and packages within their programs. While readers must understand the basics of Python programming, no knowledge of modular programming techniques is required.

"The Coder's Apprentice" is a course book, aimed at teaching Python 3 to students and teenagers who are completely new to programming. Contrary to many of the other books that teach Python programming, this book assumes no previous knowledge of programming on the part of the students, and contains numerous exercises that allow students to train their programming skills.

The book covers all imperative programming and object oriented programming aspects of Python 3, including:

This book will illustrate how and why you should learn Python to strengthen your analysis skills and efficiency as you creatively solve real-world problems through instruction-based tutorials. The tutorials use an interactive design, giving you experience of the development process so you gain a better understanding of what it means to be a forensic developer.

Discover how to perform Python script development.

Update yourself by learning the best practices in forensic programming.

Build scripts through an iterative design.

Explore the rapid development of specialized scripts.

Understand how to leverage forensic libraries developed by the community.

If you are a forensics student, hobbyist, or professional that is seeking to increase your understanding in forensics through the use of a programming language, then this book is for you.You are not required to have previous experience in programming to learn and master the content within this book. This material, created by forensic professionals, was written with a unique perspective and understanding of examiners who wish to learn programming.

This book provides a rich set of independent recipes that dive into the world of data analytics and modeling using a variety of approaches, tools, and algorithms. You will learn the basics of data handling and modeling, and will build your skills gradually toward more advanced topics such as simulations, raw text processing, social interactions analysis, and more.

Understand your data and explore the relationships between variables using Pandas and D3.js.

Explore a variety of techniques to classify and cluster outbound marketing campaign calls data of a bank using Pandas, mlpy, NumPy, and Statsmodels.

Reduce the dimensionality of your dataset and extract the most important features with pandas, NumPy, and mlpy.

Predict the output of a power plant with regression models and forecast water flow of American rivers with time series methods using pandas, NumPy, Statsmodels, and scikit-learn.

Explore social interactions and identify fraudulent activities with graph theory concepts using NetworkX and Gephi.

Scrape Internet web pages using urlib and BeautifulSoup and get to know natural language processing techniques to classify movies ratings using NLTK .

Study simulation techniques in an example of a gas station with agent-based modeling.

Who this book is written for:

This hands-on recipe guide is divided into three sections that tackle and overcome real-world data modeling problems faced by data analysts/scientist in their everyday work. Each independent recipe is written in an easy-to-follow and step-by-step fashion.

This course assumes no programming experience and slowly builds the tools you need to take on larger challenges. Once this is done, dive into the fundamentals of Python programming with variables, numbers, strings, and so on. You'll learn to make decisions on your programs with conditional statements and discover that Python has the ability to iterate over the items of any sequence such as a list or a string with loops.

You will see how functions play a major role to provide a high degree of code reusing. Along with the built-in functions, you will be able to build your own functions as well. When you've done all this, you'll be ready to create modules in Python all by yourself. Finally, you'll enhance your skills by performing some very interesting manipulations on images.

What You Will Learn:

Get to know Python’s data structures to enhance good design patterns and scalability to your code

Construct loops to perform repeated tasks

Create functions in Python to provide programs with better modularity

Understand the concept of function recursion adding clarity to write and debug codes

Manage program control flow and branching to perform conditional tasks

Kids are always the most fast-paced and enthusiastic learners, and are naturally willing to build stuff that looks like magic at the end (when it works!). Programming can be one such magic. Being able to write a program that works helps them feel they've really achieved something. Kids today are very tech-savvy and cannot wait to enter the fast-paced digital world.

Because Python is one of the most popular languages and has a syntax that is quite simple to understand, even kids are eager to use it as a stepping stone to learning programming languages.

This book covers projects that are simple and fun, and teach kids how to write Python code that works.

The book will teach the basics of Python programming, installation, and so on and then will move on to projects. A total of three projects, with each and every step explained carefully, without any assumption of previous experience.

This book is for kids (aged 10 and over). This is book is intended for absolute beginners who lack any knowledge of computing or programming languages and want to get started in the world of programming.

You’ve bested creepers, traveled deep into caves, and maybe even gone to The End and back—but have you ever transformed a sword into a magic wand? Built a palace in the blink of an eye? Designed your own color-changing disco dance floor?

In Learn to Program with Minecraft®, you’ll do all this and more with the power of Python, a free language used by millions of professional and first-time programmers!

Begin with some short, simple Python lessons and then use your new skills to modify Minecraft to produce instant and totally awesome results. Learn how to customize Minecraft to make mini-games, duplicate entire buildings, and turn boring blocks into gold.

You’ll also write programs that:

Take you on an automated teleportation tour around your Minecraft world

Build massive monuments, pyramids, forests, and more in a snap!

Make secret passageways that open when you activate a hidden switch

Create a spooky ghost town that vanishes and reappears elsewhere

Show exactly where to dig for rare blocks

Cast a spell so that a cascade of flowers (or dynamite if you’re daring!) follows your every move

Make mischief with dastardly lava traps and watery curses that cause huge floods

Whether you’re a Minecraft megafan or a newbie, you’ll see Minecraft in a whole new light while learning the basics of programming. Sure, you could spend all day mining for precious resources or building your mansion by hand, but with the power of Python, those days are over!

If you’ve ever spent hours renaming files or updating hundreds of spreadsheet cells, you know how tedious tasks like these can be. But what if you could have your computer do them for you?

In Automate the Boring Stuff with Python, you’ll learn how to use Python to write programs that do in minutes what would take you hours to do by hand—no prior programming experience required. Once you’ve mastered the basics of programming, you’ll create Python programs that effortlessly perform useful and impressive feats of automation to:

Search for text in a file or across multiple files

Create, update, move, and rename files and folders

Search the Web and download online content

Update and format data in Excel spreadsheets of any size

Split, merge, watermark, and encrypt PDFs

Send reminder emails and text notifications

Fill out online forms

Step-by-step instructions walk you through each program, and practice projects at the end of each chapter challenge you to improve those programs and use your newfound skills to automate similar tasks.

Don’t spend your time doing work a well-trained monkey could do. Even if you’ve never written a line of code, you can make your computer do the grunt work. Learn how in Automate the Boring Stuff with Python.

In Doing Math with Python you'll learn to how to use the Python programming language as a tool to delve into math concepts. Python is easy to learn, and it's perfect for exploring topics like statistics, geometry, probability, and calculus. You’ll learn to write programs to find derivatives, solve equations graphically, manipulate algebraic expressions, even examine projectile motion.

Rather than crank through tedious calculations by hand, you'll learn how to use Python functions and modules to handle the number crunching while you focus on the principles behind the math. Exercises throughout teach fundamental programming concepts, like using functions, handling user input, and reading and manipulating data. As you learn to think computationally, you'll discover new ways to explore and think about math, and gain valuable programming skills that you can use to continue your study of math and computer science.

If you’re interested in math but have yet to dip into programming, you’ll find that Python makes it easy to go deeper into the subject—let Python handle the tedious work while you spend more time on the math.

Python Crash Course is a fast-paced and thorough introduction to Python that will have you writing programs, solving problems, and making things that work, fast.

In the first half of the book you’ll learn basic programming concepts, from installing Python to testing code, including how to use a text editor, write clean and readable code, store data in lists and dictionaries, create classes that simulate real-life objects, and write loops to perform actions on your data. You’ll also learn how to make your programs interactive and test your code safely before adding it to your project. The exercises in chapters will help cement your learning so you can put that knowledge into practice in the second half, with three substantial projects: a Space-Invaders-inspired arcade game, data visualization with Python’s super-handy libraries, and a simple Web app you can deploy online.

As you work through Python Crash Course you’ll learn how to:

Make games that respond to buttons and mouse clicks, and grow more difficult as you progress

Work with data and generate interactive visualizations

Utilize the most useful Python libraries and tools, including: matplotlib, numpy, and pygal

Create and customize Web apps and deploy them safely online

Deal with mistakes and errors, to better prepare you for solving your own programming problems later

Python Crash Course will teach you just what you need to know to start programming meaningful things quickly.

The code in this book runs on almost anything: Windows, Mac, Linux, even an OLPC laptop or Raspberry Pi!

Python is a powerful, expressive programming language that’s easy to learn and fun to use! But books about learning to program in Python can be kind of dull, gray, and boring, and that’s no fun for anyone.

Python for Kids brings Python to life and brings you (and your parents) into the world of programming. The ever-patient Jason R. Briggs will guide you through the basics as you experiment with unique (and often hilarious) example programs that feature ravenous monsters, secret agents, thieving ravens, and more. New terms are defined; code is colored, dissected, and explained; and quirky, full-color illustrations keep things on the lighter side.

Chapters end with programming puzzles designed to stretch your brain and strengthen your understanding. By the end of the book you’ll have programmed two complete games: a clone of the famous Pong and “Mr. Stick Man Races for the Exit”—a platform game with jumps, animation, and much more.

As you strike out on your programming adventure, you’ll learn how to:

Use fundamental data structures like lists, tuples, and maps

Organize and reuse your code with functions and modules

Use control structures like loops and conditional statements

Draw shapes and patterns with Python’s turtle module

Create games, animations, and other graphical wonders with tkinter

Why should serious adults have all the fun? Python for Kids is your ticket into the amazing world of computer programming.

Teach Your Kids to Code is a parent's and teacher's guide to teaching kids basic programming and problem solving using Python, the powerful language used in college courses and by tech companies like Google and IBM.

Step-by-step explanations will have kids learning computational thinking right away, while visual and game-oriented examples hold their attention. Friendly introductions to fundamental programming concepts such as variables, loops, and functions will help even the youngest programmers build the skills they need to make their own cool games and applications.

Whether you've been coding for years or have never programmed anything at all, Teach Your Kids to Code will help you show your young programmer how to:

Explore geometry by drawing colorful shapes with Turtle graphics

Write programs to encode and decode messages, play Rock-Paper-Scissors, and calculate how tall someone is in Ping-Pong balls

Create fun, playable games like War, Yahtzee, and Pong

Add interactivity, animation, and sound to their apps

Teach Your Kids to Code is the perfect companion to any introductory programming class or after-school meet-up, or simply your educational efforts at home. Spend some fun, productive afternoons at the computer with your kids—you can all learn something!

Getting started with programming can be an intimidating challenge. Most books and tutorials assume lots of previous knowledge, skip over jargon and new concepts, and use examples that only make sense if you already understand programming.

This book is different. It gives you an introduction to programming in Python from the ground up, starting with tips on installation and setting up your programming environment, and moving through the core parts of the Python language in a logical order. Dr. Jones has drawn on his many years experience teaching programming to produce a book that will guide you through the language step by step in simple terms.

The book doesn't assume any previous knowledge, and introduces fundamental programming concepts like variables, loops and functions using simple terms and easy-to-follow examples that you can run and modify.

The book takes a unique approach to practical exercises. Rather than simply presenting you with the solutions, it shows you how large, complex programs are gradually built up from simple building blocks, explaining the role of every line. You can download the examples and exercise solutions - edit, modify and run them yourself.

Geospatial data is hard to ignore. Nearly every car, phone, or camera has a GPS sensor, and aerial photos, satellite imagery, and data representing political boundaries, roads, rivers, and streams are available for free download from many websites. Geoprocessing is the science of reading, analyzing, and presenting geospatial data programmatically. The Python language, along with dozens of open source libraries and tools, makes it possible to take on professional geoprocessing tasks without investing in expensive proprietary packages like ArcGIS and MapInfo.

Geoprocessing with Python teaches you how to use the Python programming language along with free and open source tools to read, write, and process geospatial data. You'll learn how to access available data sets to make maps or perform your own analyses using free and open source tools like the GDAL, Shapely, and Fiona Python modules. You'll master core practices like handling multiple vector file formats, editing and manipulating geometries, applying spatial and attribute filters, working with projections, and performing basic analyses on vector data. You'll also learn how to create geospatial data, rather than just consuming it. The book also covers how to manipulate and analyze raster data, such as aerial photographs, satellite images, and digital elevation models.

The book is a gentle introtuction to algorithms and programming with Python, specially for those that don't know how to program on any language. It has a progressive approach, it covers most of basic topics like programming logics, repetition structures. It is a small book, but yet a powerful tool for learning. It helps get good orientation from the beginning. It has only 100 pages and it's free.

An introduction to Python with data structures (CS2). Covers the design of collection classes with polymorphism and inheritance, multiple implementations of collection interfaces, and the analysis of space/time tradeoffs of different collections, all within a realistic collection framework.

Basic Python Programming.

An Overview of Collections.

Searching, Sorting, and Complexity Analysis.

Arrays and Linked Structures.

Interfaces, Implementations, and Polymorphism.

Inheritance and Abstract Classes.

Stacks.

Queues.

Lists.

Trees.

Sets and Dictionaries.

Graphs.

Link to example programs and other information at the author's website.

An introduction to GUI programming in Python. Topics include the basics of window layout, widget configuration, and responding to user events. The book uses a toolkit, breezypythongui, to explore GUI concepts and resources within a simple coding framework.

The Hello World Program

Windows, Layouts, and Window Components

Command Buttons and Responding to Events

Input and Output with Data Fields

Error Handling and Message Boxes

The Model/View/Controller Pattern

Check Buttons, Radio Buttons, and Menus

Scrolling List Boxes

Text Areas and File Dialogs

Dialogs

Canvases and Graphics Operations

Responding to Mouse Events in Canvases

Link to example programs and ebook vendor sites at the author's website.

Master the art of machine learning with Python and build effective machine learning systems with this intensive hands-on guide

Master Machine Learning using a broad set of Python libraries and start building your own Python-based ML systems

Covers classification, regression, feature engineering, and much more guided by practical examples

A scenario-based tutorial to get into the right mind-set of a machine learner (data exploration) and successfully implement this in your new or existing projects

Who this book was written for

This book is for Python programmers who are beginners in machine learning, but want to learn Machine learning. Readers are expected to know Python and be able to install and use open-source libraries. They are not expected to know machine learning, although the book can also serve as an introduction to some Python libraries for readers who know machine learning. This book does not go into the detail of the mathematics behind the algorithms.

This book primarily targets Python developers who want to learn and build machine learning in their projects, or who want to provide machine learning support to their existing projects, and see them getting implemented effectively.

An introduction to computer programming, using the easy, yet powerful, Python programming language. Python, a cross-platform language used by such organizations as Google and NASA, lets you work quickly and efficiently, allowing you to concentrate on your work rather than the language. The core Python language (both versions 2.x and 3.x) is discussed, as well as an introduction to graphical user interface creation.

The ideas covered in this book provide the reader with many major programming topics, applicable to a wide variety of programming languages. After reading this book, the reader should be able to quickly create simple to medium-level programs and be prepared to tackle more complex programming tasks.

An in-depth, tutorial introduction to Python core language fundamentals, based on 260 live classes taught by the author. This edition is updated to cover both Python 3.X and 2.X. It is specifically based on 3.3 and 2.7, but is applicable to other releases.

An in-depth, tutorial introduction to common Python application programming domains, and a follow-up to the core language coverage of Learning Python. This edition is updated to use Python 3.X (3.1 and 3.2 specifically), but is still largely applicable to most 2.X readers.

A reference-only book, designed to serve as a companion to both Learning Python and Programming Python. This edition is updated to cover both Python 3.X and 2.X. It is specifically based on 3.1 and 2.6, but is applicable to other releases.

Ever wished you could learn Python from a book? Head First Python helps you learn the language through a unique method that goes beyond syntax and how-to manuals. You'll quickly grasp Python's fundamentals, then move on to persistence, exception handling, web development, SQLite, data wrangling, and Google App Engine. You'll also learn how to write mobile apps for Android, all thanks to the power that Python gives you. Head First Python is a complete learning experience that will help you become a bona fide Python programmer.

This book is designed to get you up to speed with Python as quickly as possible.

Computers are used in every part of science from ecology to particle physics. This introduction to computer science continually reinforces those ties by using real-world science problems as examples. Anyone who has taken a high school science class will be able to follow along as the book introduces the basics of programming, then goes on to show readers how to work with databases, download data from the web automatically, build graphical interfaces, and most importantly, how to think like a professional programmer.

Practical Programming: An Introduction to Computer Science Using Python

ISBN: 978-1-93435-627-2

350 pages, Apr 2009

Computers are used in every part of science from ecology to particle physics. This introduction to computer science continually reinforces those ties by using real-world science problems as examples. Anyone who has taken a high school science class will be able to follow along as the book introduces the basics of programming, then goes on to show readers how to work with databases, download data from the web automatically, build graphical interfaces, and most importantly, how to think like a professional programmer.

Who this book is written for If you're new to Object Oriented Programming techniques, or if you have basic Python skills and wish to learn in depth how and when to correctly apply Object Oriented Programming in Python, this is the book for you.

If you are an object-oriented programmer for other languages, you too will find this book a useful introduction to Python, as it uses terminology you are already familiar with.

Python 2 programmers seeking a leg up in the new world of Python 3 will also find the book beneficial, and you need not necessarily know Python 2.

The Computer Science Department of Michigan State University converted their Introduction to Programming Course CSE 231 to Python in the Fall of 2007. One of the products of this change was this textbook, written as a general introduction to CS1 using Python. The book adopts the theme of "data manipulation" for its examples, focusing on using real-world datasets and manipulating them (averages, graphs, indicies, searches, etc.) in various ways.

The book covers the standard CS1 curriculum, and includes extensive algorithm development sections to help students in their study of computing. Supplemental material is also provided including: full set of power point slides, collaborative lab exercises, project homeworks and solutions to over 600 exercises in the book.

Ever wished you could learn how to program from a book? If you have no previous programming experience, you might be wondering where to start. Head First Programming introduces the core concepts of writing computer programs--variables, decisions, loops, functions, and objects--which apply regardless of the programming language, but uses concrete examples and exercises in the dynamic and versatile Python language to apply and reinforce these concepts.

Learn the basic tools to start writing the programs that interests you, not the generic software someone else thinks you should have, and get a better understanding of what software can (and cannot) do. When you're finished, you'll have the necessary foundation to apply to whatever language or software project you need or want to learn.

This book teaches you how to write programs using Python 3 in good Python 3 style.

The book will be useful to people who program professionally as part of their job, whether as full-time software developers, or those from other disciplines, including scientists and engineers, who need to do some programming in support of their work. It will also prove ideal for those Python 2 programmers who need to migrate (or prepare to migrate) to Python 3. The book is also suitable for students—the only prerequisite is some basic knowledge of programming in any language, for example, Basic or Java, or of course Python itself.

The book teaches solid procedural style programming, then builds on that to teach solid object-oriented programming, and then goes on to more advanced topics such as descriptors and class decorators. But even newcomers to Python 3 should be able to write useful (although small and basic) programs after reading chapter 1, and then go on to create larger and more sophisticated programs as they work through the chapters.

The book's web site lists the table of contents and has links to extracts. It also has all the examples and exercise solutions available for download.

Python for Software Design is a concise introduction to software design using the Python programming language. Intended for people with no programming experience, this book starts with the most basic concepts and gradually adds new material. Some of the ideas students find most challenging, like recursion and object-oriented programming, are divided into a sequence of smaller steps and introduced over the course of several chapters. The focus is on the programming process, with special emphasis on debugging. The book includes a wide range of exercises, from short examples to substantial projects, so that students have ample opportunity to practice each new concept. Exercise solutions and code examples are available from thinkpython.com, along with Swampy, a suite of Python programs that is used in some of the exercises.

"Computer programming is a powerful tool for children to 'learn learning,' that is, to learn the skills of thinking and problem-solving...Children who engage in programming transfer that kind of learning to other things."--Nicholas Negroponte, the man behind the One Laptop Per Child project that hopes to put a computer in the hands of every child on earth, January 2008

Your computer won't respond when you yell at it. Why not learn to talk to your computer in its own language? Whether you want to write games, start a business, or you're just curious, learning to program is a great place to start. Plus, programming is fun!

Hello World! provides a gentle but thorough introduction to the world of computer programming. It's written in language a 12-year-old can follow, but anyone who wants to learn how to program a computer can use it. Even adults. Written by Warren Sande and his son, Carter, and reviewed by professional educators, this book is kid-tested and parent-approved. You don't need to know anything about programming to use the book. But you should know the basics of using a computer--e-mail, surfing the web, listening to music, and so forth. If you can start a program and save a file, you should have no trouble using this book.

Color ebook and black and white print book are both available from the publisher at www.manning.com/sande.

Hello! Python fully covers the building blocks of Python programming and gives you an introduction to more advanced topics such as object-oriented programming, functional programming, network programming, and program design. New (or nearly new) programmers will learn most of what they need to know to start using Python immediately.

A more advanced book than Hello World!, it takes a different tack. You follow along as the author writes a practical project in every chapter. You'll see how programmers think about solving problems with programming, and there are hints on how to extend the projects to suit your own needs.

This revision of Manning's popular The Quick Python Book offers a clear, crisp introduction to the elegant Python programming language and its famously easy-to-read syntax. Written for programmers new to Python, this updated edition covers features common to other languages concisely, while introducing Python's comprehensive standard functions library and unique features in detail.

After exploring Python's syntax, control flow, and basic data structures, the book shows how to create, test, and deploy full applications and larger code libraries. It addresses established Python features as well as the advanced object-oriented options available in Python 3. Along the way, you'll survey the current Python development landscape, including GUI programming, testing, database access, and web frameworks.

IronPython is an implementation of Python for the Microsoft .NET framework, Mono, and the Silverlight and Moonlight browser plugins. IronPython in Action is an introduction to programming with IronPython for both .NET programmers interested in Python and Python programmers new to .NET.

IronPython in Action includes a swift paced Python tutorial, chapters introducing .NET libraries and structured application development with Python, integrating with other .NET languages like C# and VB.NET, server side web programming with ASP.NET and client side web programming with Silverlight, system administration, working with the WPF and Windows Forms user interface libraries and embedding the IronPython engine in .NET applications.

As well as covering specific topics and both Python and .NET libraries the book pays special attention to the nitty-gritty details of Python and .NET integration that previous experience with Python or C# won't necessarily have prepared you for.

Color ebook and black and white print book are both available from the publisher at www.manning.com/foord.

Gain a fundamental understanding of Python’s syntax and features with the second edition of Beginning Python, an up–to–date introduction and practical reference. Covering a wide array of Python–related programming topics, including addressing language internals, database integration, network programming, and web services, you’ll be guided by sound development principles. Ten accompanying projects will ensure you can get your hands dirty in no time.

Updated to reflect the latest in Python programming paradigms and several of the most crucial features found in the forthcoming Python 3.0 (otherwise known as Python 3000), advanced topics, such as extending Python and packaging/distributing Python applications, are also covered.

Following the usual Dummies style, this book takes a light-hearted approach to introducing Python. In addition to Python itself, Python for Dummies also gives lots of advice about good programming practices.

Introduction to Computing and Programming Using Python: A Multimedia Approach Mark Guzdial

The main goal of this book is comprehensively teaching you the core of the Python language, much more than just its syntax (which you don't really need a book to learn, right?). Knowing more about how Python works under the covers, including the relationship between data objects and memory management, will make you a much more effective Python programmer coming out of the gate. The advanced topics chapters are meant as complete intros or "quick dives" into each of those distinct subjects. However, if moving towards those specific areas of development, they are more than enough to get you pointed in the right direction. We would say that the book is 40% introductory, 40% intermediate (in-depth core Python material plus advanced topics chapters), and 20% reference -- it is *not* meant to be a substitute for a pure reference such as the Python Essential Reference or Python in a Nutshell.

The new 2nd edition is expanded (300 new pages!) and updated through Python 2.5 as well as confirmed functionality for future versions! Also added are a few new chapters of advanced material. As in the 1st edition, a plethora of easy to advanced exercises can be found at the end of every chapter to hammer the concepts home. At the moment, this is the most complete and up-to-date Python book on the market today.

From an anonymous reviewer: "Very well written. It is the clearest, friendliest book I have come across yet for explaining Python, and putting it in a wider context. It does not presume a large amount of other experience. It may be too slow for more advanced people, but it does go into some important Python topics carefully and in depth. Unlike too many beginner books, it never condescends or tortures the reader with childish hide-and-seek prose games. Not too many in-depth real-world examples in the book (hopefully he will do a followup volume), it sticks to gaining a solid grasp of Python syntax and structure."

Home Page (includes book reviews, errata, sample chapter, links to alternate editions, source code from the book, and more!)

Dive Into Python is a free Python ebook for experienced programmers available under the GNU Free Documentation License. A printed version has been published by Apress http://www.apress.com/, and is available through all major outlets.

Learning Python is meant for beginning Python programmers, and others seeking a quick introduction to the language. It focuses on core language fundamentals in depth, is based on Mark Lutz's Python training classes, and includes numerous exercises with solutions to guide the reader through a hands-on learning experience.

A Python classic, updated and expanded to cover Python 2.5. The first edition, published in 1996, was the first Python book project to be signed. Programming Python is about what you can do with Python after you've mastered the language fundamentals - it assumes you already know the core language, and focuses on applications programming in gradual tutorial fashion. It is designed to be a natural follow-up to the book Learning Python. This book includes 300 pages on GUIs, 500 on Internet programming, and more on databases, systems programming, text processing, Python/C integration, and other topics. Also available in PDF form from O'Reilly.

Based in part on 3,000 newsgroup articles written by Python veteran FredrikLundh over the last four and half years, this book provides sample scripts for all standard modules in the Python library. Also available in German.

The book has introductory chapters on Python, networking, Apache, Linux, and MySQL. It is a self- contained reference to Python and open-source programming that makes use of Python to develop real applications that are also available under an open source license.

A Byte of Python is a book on programming using the Python language. It serves as a tutorial or guide to the Python language for anyone. If all you know is how to save text files, then you can learn Python using this book. If you are an expert programmer who loves C, Perl, Java or C#, you can also learn Python using this book.

The first half of this book introduces the Python language, and the second half demonstrates its usage in various practical projects such as "automated document conversion, newsgroup administration, graphical PDF document generation, remote document maintenance, the creation of a peer-to-peer system with XML-RPC, database integration, and GUI and game development." A new edition of this book is available under the title Beginning Python: From Novice to Professional.

Written by a homeschooling Dad for teenage youth, this introductory computer programming book is for people who have no prior programming experience. Teaches the basic principles of programming using Python, with lots of examples. Small video game project at the end. Good for self-study or classroom use.

The 'Python First' digital pack provides a gentle introduction to computer science. It is more than a book: Ten self-contained online chapters consist of e-texts, slides, 62 labs, tens of sample programs, and online quizzes. The 'Python First' pack includes a wealth of detailed self-guided labs that you can complete on your own.

This book is a paper companion to the "Python First" digital pack from studypack.com. The complete digital study pack features e-texts, slides, a wealth of detailed self-guided labs that learners can complete on their own, sample programs, and extensive quizzes. The book offers a printed version of the e-texts and self-guided labs in the same format as they appear in the online digital pack.

How do you learn Python? By doing a series of exercises, each of which adds a single new feature of the language. This 250+ page book has 31 chapters that will help you build Python programming skills through a series of exercises. This book includes six projects from straight-forward to sophisticated that will help solidify your Python skills.

Building Skills in Programming How To Write Your Own Software Using Python

Steven F. Lott

How do you learn to solve your own programs by writing programs? By doing a series of exercises, each of which builds up a part of the skill set we call "computer programming". This book has 54 chapters that will help you build basic programming skills through a series of exercises that grow from simple identification of the parts of your computer through to statistical simulations.

How do you move from OO programming to OO design? Do a lot of design focused on building a sophisticated application program. This 269-page book has 43 chapters that will help you build OO design skills through the creation of a moderately complex family of application programs. This is a step-by-step guide to OO design and implementation using either the Java or Python programming languages.

Python Programming in Context is a clear, accessible introduction to the fundamental programming and problem solving concepts necessary for students at this level. The authors carefully build upon the many important computer science concepts and problem solving techniques throughout the text and offer relevant, real-world examples and exercises to reinforce key material. Programming skills throughout the text are linked to applied areas such as Image Processing, Cryptography, Astronomy, Music, the Internet, and Bioinformatics, giving students a well rounded look of its capabilities.

Python is a very powerful high level object oriented language and has easy bindings with C,C++,Java etc. It can very well be the first and the only language needed by a software tester for routine test automation tasks as well as for building robust general purpose test automation frameworks.

This online book is written keeping beginners in Python in mind. It bridges the gap between very basic tutorials and comprehensive books.

Practical Programming (in Python)"Practical Programming (in Python)" is meant as a first programming course and is tightly aligned with the University of Otago introductory programming course called Practical Programming. The textbook is organized into 24 "lectures" that cover all the basics of programming (sequence, selection, iteration, functions etc), plus all the major data structures supplied by Python.

'Start Here: Python Programming for Beginners"Start Here: Python Programming for beginners" by Jody S. Ginther was written after the changes made in Python 3. It is aimed at total beginners and uses hands-on practice and humor to make learning to program more relaxing and enjoyable.

This course introduces the student to Python, cultivating professional software development skills along the way. Yet, this course requires no prior programming knowledge to learn or to teach. Organized as sixty-four lessons, sixteen tests and two exams, for a total of 82 lecture units, this course covers the following topics:

Python variable, function, class and environment syntax.

Custom module development.

Software development skills and activities.

Python dictionaries, lists, tuples and iterable types.

Command console operations.

List comprehensions.

Python exception handling.

IPython operations and commands.

Essential built-in Python library features.

An introduction to Matplotlib.

An introduction to wxPython and windowed development.

By the end of the course, students will have learned how to write Python projects, including custom classes and modules. The course culminates with an exploration of windowed application development, including a bitmapped graphics project. The course material includes the student text and CD-ROM with instructor's guide for each lesson, tests, exams, solutions guide for exercises, tests and exams, student handouts and completed projects for each lesson. The format of the course is intended to support classroom use, homeschool use, or self-study.

Introduction to Computer Science Using Python: A Computational Problem-Solving Focus

Recommended by Guido van Rossum "This is not your average Python book ... The book is incredibly thorough: there are exercises throughout the text (not just at the end of each chapter), and it includes a plethora of examples, screenshots, tables, charts, diagrams, and photos ... I love the final chapter, which is an overview of the history of computing, starting with Charles Babbage and Ada Lovelace ... All in all, I think this book is a great text for anyone teaching CS1." Neopythonic

Recommended by Textbook Adopter Herbert Daly - University of Bedfordshire "As a University lecturer with considerable responsibility at level 1, I found this book to be an outstanding introduction to the world of computing and the Python language." Amazon Review

Features:

Basics first approach

Focus on computational problem solving, starting with Chapter 1

Hands-on "Let's Try It" sections and self-test questions throughout the book

Each chapter section contains a "Let's Apply It" example program

Each chapter ends with the step-by-step development and demonstrated debugging techniques of a significant-size program

Contains various end-of-chapter exercises and assignments, including simple programming exercises, assignments involving the modification of programs from within the chapter, and challenging program development problems

Thirty-four page final chapter on the history of computing, starting with Charles Babbage, with over 65 historical images

Python 3 Programmers' Reference at the end of the book for quick look-up of Python details

Little time to learn Python? This book shows you how to learn Python during your coffee break!

Coffee Break Python is a new step-by-step learning system where you repeatedly solve practical Python puzzles.

Educational research shows that practical low-stake puzzles and tests help you to learn faster, smarter, and better.

As you work through Coffee Break Python, your Python expertise will grow—one coffee at a time. The book is packed with 50 Python puzzles, 10 practical learning tips, 5 compressed cheat sheets, and 1 new way to measure your coding skills.